Methods for Autoregistration of Arthroscopic Video Images to Preoperative Models and Devices Thereof

ABSTRACT

Surgical methods and devices that facilitate registration of arthroscopic video to preoperative models are disclosed. With this technology, a machine learning model is applied to diagnostic video data captured via an arthroscope to identify an anatomical structure. An anatomical structure in a three-dimensional (3D) anatomical model is registered to the anatomical structure represented in the diagnostic video data. The 3D anatomical model is generated from preoperative image data. The anatomical structure is then tracked intraoperatively based on the registration and without requiring fixation of fiducial markers to the patient anatomy. A simulated projected view of the registered anatomical structure is generated from the 3D anatomical model based on a determined orientation of the arthroscope during capture of intraoperative video data. The simulated projected view is scaled and oriented based on one or more landmark features of the anatomical structure extracted from the intraoperative video data.

This application claims the benefit of U.S. Provisional ApplicationSerial No. 63/040,664, filed on Jun. 18, 2020, and U.S. ProvisionalApplication Serial No. 63/111,844, filed on Nov. 10, 2020, the contentsof which are incorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates generally to methods, systems, andapparatuses related to a computer-assisted surgical system that includesvarious hardware and software components that work together to enhancesurgical workflows. The disclosed techniques may be applied to, forexample, shoulder, hip, and knee arthroplasties, as well as othersurgical interventions such as arthroscopic procedures, spinalprocedures, maxillofacial procedures, rotator cuff procedures, ligamentrepair and replacement procedures.

BACKGROUND

During computer navigated surgical procedures, various methods anddevices are used to track the positions of a patient’s anatomy andsurgical devices within the operating environment, and surgical trackingis an essential component of navigational surgery systems. Inparticular, knowing where the surgical tool(s) and/or anatomicalstructure surface boundaries are in three-dimensional space is a corerequirement of the current set of robotic-enabled surgical procedures inwhich precise bone modification is performed with the aid of computerassisted surgical systems (CASSs).

However, optical (e.g., infrared), electromagnetic, and mechanicaltracking systems all have certain drawbacks. Optical tracking systemsrequire that a line-of-sight be maintained between the tracking deviceand the instrument to be tracked, which is not always feasible in anoperating environment and may preclude tracking of surgical instrumentsinside the body. Optical tracking systems also are only accurate withina defined volume with respect to camera position, which can be difficultto maintain throughout a surgical procedure.

Electromagnetic tracking systems similarly only provide accuratemeasurements within a defined volume with respect to the position of thefield generator. Further, metal in the electromagnetic field, which iscommonly used during orthopedic and sports medicine procedures inparticular, can generate interference and degrade the accuracy of themeasurement.

As with optical and electromagnetic tracking systems, mechanicaltracking systems also require components (e.g., tracking fiducials) tobe attached physically to the patient anatomy, which can require pins,clamps, or other attachment mechanisms that damage the anatomy. Damageto patient anatomy results in a higher risk of complications from asurgical procedure. Accordingly, current surgical tracking systems usedin navigated surgical procedures each have significant drawbacks andconstraints.

SUMMARY

Surgical computing devices, systems, non-transitory computer readablemedia, and methods that facilitate automatic registration ofarthroscopic video images to preoperative models are disclosed.According to some embodiments, a machine learning model is applied todiagnostic video data captured via an arthroscope to identify ananatomical structure represented in the diagnostic video data. One of aplurality of anatomical structures in a three-dimensional (3D)anatomical model is registered to the anatomical structure representedin the diagnostic video data. The 3D anatomical model is generated frompreoperative image data. The anatomical structure is then trackedintraoperatively based on the registration. A simulated projected viewof the registered one of the plurality of anatomical structures isgenerated from the 3D anatomical model based on a determined orientationof the arthroscope during capture by the arthroscope of intraoperativevideo data. The simulated projected view is scaled and oriented based onone or more landmark features of the anatomical structure extracted fromthe intraoperative video data. The scaled and oriented simulatedprojected view is then output, such as to a display device (e.g., amixed reality headset).

According to some embodiments, an overlay is generated that includes thescaled and oriented simulated projected view. The generated overlay isthen merged with the intraoperative video data based on the registrationto generate merged video data, which is output to the display device.

According to some embodiments, a stage of a surgical procedure isdetermined based on an obtained surgical plan for the surgical procedureand identification of the anatomical structure in the intraoperativevideo data. The generated overlay further includes guidance extractedfrom the obtained surgical plan. In one or more of these embodiments,the guidance includes textual directions associated with a current taskin the surgical procedure or a visual indication of another one of theplurality of anatomical structures corresponding to a subsequent task inthe surgical procedure.

According to some embodiments, an annotated version of the 3D anatomicalmodel or the preoperative image data is obtained that identifies ananatomical point corresponding to a portion of patient anatomy or atleast one of the one or more landmark features. In one or more of theseembodiments, the generated overlay further includes an indication of theanatomical point.

According to some embodiments, the machine learning model is trainedbased on additional video data including a plurality of image frameseach including at least one annotated representation of one or more ofthe plurality of anatomical structures.

According to some embodiments, the display device includes a mixedreality headset. In one or more of these embodiments, one or more of aposition or an orientation of the mixed reality headset is tracked. Theoverlay is then generated using a field of view of the arthroscope todetermine a local reference frame and based on a known spatial and scalerelationship between the arthroscope field of view and another referenceframe of the mixed reality headset determined based on the tracking.

According to some embodiments, at least a portion of the simulatedprojected view includes another one of the plurality of anatomicalstructures that is occluded in another field of view of the mixedreality headset.

According to some embodiments, an eye position of a user is determinedand the scaled and oriented simulated projected view is output to aprojector for projection onto patient skin based on the determined eyeposition.

According to some embodiments, the generated overlay includes adepiction of a tool or tool tip oriented according to the surgical plan,according to the determined stage of the surgical procedure, or tofacilitate optimal access to a particular portion of patient anatomy.

According to some embodiments, the anatomical structure represented inthe intraoperative video data includes soft tissue. In one or more ofthese embodiments, a size and position of a first portion of the softtissue is determined from the intraoperative video data. Another machinelearning model is then applied to the 3D anatomical model and thedetermined size and position to generate a representation of a secondportion of the soft tissue in a morphed state. Additionally, thesimulated projected view includes the representation of the secondportion of the soft tissue in the morphed state.

According to some embodiments, an additional one of the plurality ofanatomical structures from the 3D anatomical model is registered to theadditional one of the plurality of anatomical structures represented inthe intraoperative video data based on the registration of the one ofthe plurality of anatomical structures.

According to some embodiments, a weighting value is generated for eachof a plurality of portions of the 3D anatomical model. In one or more ofthese embodiments, the simulated projected view is generated to includea subset of the plurality of portions based on the weighting values.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and form a part ofthe specification, illustrate the embodiments of the invention andtogether with the written description serve to explain the principles,characteristics, and features of the invention. In the drawings:

FIG. 1 depicts an operating theatre including an illustrativecomputer-assisted surgical system (CASS) in accordance with anembodiment.

FIG. 2 depicts an example of an electromagnetic sensor device accordingto some embodiments.

FIG. 3A depicts an alternative example of an electromagnetic sensordevice, with three perpendicular coils, according to some embodiments.

FIG. 3B depicts an alternative example of an electromagnetic sensordevice, with two nonparallel, affixed coils, according to someembodiments.

FIG. 3C depicts an alternative example of an electromagnetic sensordevice, with two nonparallel, separate coils, according to someembodiments.

FIG. 4 depicts an example of electromagnetic sensor devices and apatient bone according to some embodiments.

FIG. 5A depicts illustrative control instructions that a surgicalcomputer provides to other components of a CASS in accordance with anembodiment.

FIG. 5B depicts illustrative control instructions that components of aCASS provide to a surgical computer in accordance with an embodiment.

FIG. 5C depicts an illustrative implementation in which a surgicalcomputer is connected to a surgical data server via a network inaccordance with an embodiment.

FIG. 6 depicts an operative patient care system and illustrative datasources in accordance with an embodiment.

FIG. 7A depicts an illustrative flow diagram for determining apre-operative surgical plan in accordance with an embodiment.

FIG. 7B depicts an illustrative flow diagram for determining an episodeof care including pre-operative, intraoperative, and post-operativeactions in accordance with an embodiment.

FIG. 7C depicts illustrative graphical user interfaces including imagesdepicting an implant placement in accordance with an embodiment.

FIG. 8 depicts a flowchart of an exemplary method for training a machinelearning model to facilitate automatic registration of arthroscopicvideo images to preoperative models.

FIG. 9 depicts a flowchart of an exemplary method for applying a machinelearning model to register anatomical structures represented indiagnostic arthroscopic video data and preoperative three-dimensionalanatomical models.

FIG. 10 depicts a flowchart of an exemplary method for trackingregistered anatomical structures in intraoperative video data andproviding simulated projected views generated from preoperativethree-dimensional anatomical models.

FIG. 11 depicts a flowchart of an exemplary method for generating mergedvideo data based on registration of anatomical structures and includingoverlays generated to include analytical information according to astate of a surgical procedure.

FIG. 12 depicts a block diagram of an illustrative surgical computingdevice in which aspects of the illustrative embodiments are implemented.

DETAILED DESCRIPTION

This disclosure is not limited to the particular systems, devices andmethods described, as these may vary. The terminology used in thedescription is for the purpose of describing the particular versions orembodiments only and is not intended to limit the scope.

As used in this document, the singular forms “a,” “an,” and “the”include plural references unless the context clearly dictates otherwise.Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of ordinary skillin the art. Nothing in this disclosure is to be construed as anadmission that the embodiments described in this disclosure are notentitled to antedate such disclosure by virtue of prior invention. Asused in this document, the term “comprising” means “including, but notlimited to.”

Definitions

For the purposes of this disclosure, the term “implant” is used to referto a prosthetic device or structure manufactured to replace or enhance abiological structure. For example, in a total hip replacement procedurea prosthetic acetabular cup (implant) is used to replace or enhance apatients worn or damaged acetabulum. While the term “implant” isgenerally considered to denote a man-made structure (as contrasted witha transplant), for the purposes of this specification an implant caninclude a biological tissue or material transplanted to replace orenhance a biological structure.

For the purposes of this disclosure, the term “real-time” is used torefer to calculations or operations performed on-the-fly as events occuror input is received by the operable system. However, the use of theterm “real-time” is not intended to preclude operations that cause somelatency between input and response, so long as the latency is anunintended consequence induced by the performance characteristics of themachine.

Although much of this disclosure refers to surgeons or other medicalprofessionals by specific job title or role, nothing in this disclosureis intended to be limited to a specific job title or function. Surgeonsor medical professionals can include any doctor, nurse, medicalprofessional, or technician. Any of these terms or job titles can beused interchangeably with the user of the systems disclosed hereinunless otherwise explicitly demarcated. For example, a reference to asurgeon also could apply, in some embodiments to a technician or nurse.

The systems, methods, and devices disclosed herein are particularly welladapted for surgical procedures that utilize surgical navigationsystems, such as the NAVIO® surgical navigation system. NAVIO is aregistered trademark of BLUE BELT TECHNOLOGIES, INC. of Pittsburgh, PA,which is a subsidiary of SMITH & NEPHEW, INC. of Memphis, TN.

CASS Ecosystem Overview

FIG. 1 provides an illustration of an example computer-assisted surgicalsystem (CASS) 100, according to some embodiments. As described infurther detail in the sections that follow, the CASS uses computers,robotics, and imaging technology to aid surgeons in performingorthopedic surgery procedures such as total knee arthroplasty (TKA) ortotal hip arthroplasty (THA). For example, surgical navigation systemscan aid surgeons in locating patient anatomical structures, guidingsurgical instruments, and implanting medical devices with a high degreeof accuracy. Surgical navigation systems such as the CASS 100 oftenemploy various forms of computing technology to perform a wide varietyof standard and minimally invasive surgical procedures and techniques.Moreover, these systems allow surgeons to more accurately plan, trackand navigate the placement of instruments and implants relative to thebody of a patient, as well as conduct pre-operative and intra-operativebody imaging.

An Effector Platform 105 positions surgical tools relative to a patientduring surgery. The exact components of the Effector Platform 105 willvary, depending on the embodiment employed. For example, for a kneesurgery, the Effector Platform 105 may include an End Effector 105B thatholds surgical tools or instruments during their use. The End Effector105B may be a handheld device or instrument used by the surgeon (e.g., aNAVIO® hand piece or a cutting guide or jig) or, alternatively, the EndEffector 105B can include a device or instrument held or positioned by aRobotic Arm 105A. While one Robotic Arm 105A is illustrated in FIG. 1 ,in some embodiments there may be multiple devices. As examples, theremay be one Robotic Arm 105A on each side of an operating table T or twodevices on one side of the table T. The Robotic Arm 105A may be mounteddirectly to the table T, be located next to the table T on a floorplatform (not shown), mounted on a floor-to-ceiling pole, or mounted ona wall or ceiling of an operating room. The floor platform may be fixedor moveable. In one particular embodiment, the robotic arm 105A ismounted on a floor-to-ceiling pole located between the patient’s legs orfeet. In some embodiments, the End Effector 105B may include a sutureholder or a stapler to assist in closing wounds. Further, in the case oftwo robotic arms 105A, the surgical computer 150 can drive the roboticarms 105A to work together to suture the wound at closure.Alternatively, the surgical computer 150 can drive one or more roboticarms 105A to staple the wound at closure.

The Effector Platform 105 can include a Limb Positioner 105C forpositioning the patient’s limbs during surgery. One example of a LimbPositioner 105C is the SMITH AND NEPHEW SPIDER2 system. The LimbPositioner 105C may be operated manually by the surgeon or alternativelychange limb positions based on instructions received from the SurgicalComputer 150 (described below). While one Limb Positioner 105C isillustrated in FIG. 1 , in some embodiments there may be multipledevices. As examples, there may be one Limb Positioner 105C on each sideof the operating table T or two devices on one side of the table T. TheLimb Positioner 105C may be mounted directly to the table T, be locatednext to the table T on a floor platform (not shown), mounted on a pole,or mounted on a wall or ceiling of an operating room. In someembodiments, the Limb Positioner 105C can be used in non-conventionalways, such as a retractor or specific bone holder. The Limb Positioner105C may include, as examples, an ankle boot, a soft tissue clamp, abone clamp, or a soft-tissue retractor spoon, such as a hooked, curved,or angled blade. In some embodiments, the Limb Positioner 105C mayinclude a suture holder to assist in closing wounds.

The Effector Platform 105 may include tools, such as a screwdriver,light or laser, to indicate an axis or plane, bubble level, pin driver,pin puller, plane checker, pointer, finger, or some combination thereof.

Resection Equipment 110 (not shown in FIG. 1 ) performs bone or tissueresection using, for example, mechanical, ultrasonic, or lasertechniques. Examples of Resection Equipment 110 include drillingdevices, burring devices, oscillatory sawing devices, vibratoryimpaction devices, reamers, ultrasonic bone cutting devices, radiofrequency ablation devices, reciprocating devices (such as a rasp orbroach), and laser ablation systems. In some embodiments, the ResectionEquipment 110 is held and operated by the surgeon during surgery. Inother embodiments, the Effector Platform 105 may be used to hold theResection Equipment 110 during use.

The Effector Platform 105 also can include a cutting guide or jig 105Dthat is used to guide saws or drills used to resect tissue duringsurgery. Such cutting guides 105D can be formed integrally as part ofthe Effector Platform 105 or Robotic Arm 105A, or cutting guides can beseparate structures that can be matingly and/or removably attached tothe Effector Platform 105 or Robotic Arm 105A. The Effector Platform 105or Robotic Arm 105A can be controlled by the CASS 100 to position acutting guide or jig 105D adjacent to the patient’s anatomy inaccordance with a pre-operatively or intraoperatively developed surgicalplan such that the cutting guide or jig will produce a precise bone cutin accordance with the surgical plan.

The Tracking System 115 uses one or more sensors to collect real-timeposition data that locates the patient’s anatomy and surgicalinstruments. For example, for TKA procedures, the Tracking System mayprovide a location and orientation of the End Effector 105B during theprocedure. In addition to positional data, data from the Tracking System115 also can be used to infer velocity/acceleration ofanatomy/instrumentation, which can be used for tool control. In someembodiments, the Tracking System 115 may use a tracker array attached tothe End Effector 105B to determine the location and orientation of theEnd Effector 105B. The position of the End Effector 105B may be inferredbased on the position and orientation of the Tracking System 115 and aknown relationship in three-dimensional space between the TrackingSystem 115 and the End Effector 105B. Various types of tracking systemsmay be used in various embodiments of the present invention including,without limitation, Infrared (IR) tracking systems, electromagnetic (EM)tracking systems, video or image based tracking systems, and ultrasoundregistration and tracking systems. Using the data provided by thetracking system 115, the surgical computer 150 can detect objects andprevent collision. For example, the surgical computer 150 can preventthe Robotic Arm 105A and/or the End Effector 105B from colliding withsoft tissue.

Any suitable tracking system can be used for tracking surgical objectsand patient anatomy in the surgical theatre. For example, a combinationof IR and visible light cameras can be used in an array. Variousillumination sources, such as an IR LED light source, can illuminate thescene allowing three-dimensional imaging to occur. In some embodiments,this can include stereoscopic, tri-scopic, quad-scopic, etc. imaging. Inaddition to the camera array, which in some embodiments is affixed to acart, additional cameras can be placed throughout the surgical theatre.For example, handheld tools or headsets worn by operators/surgeons caninclude imaging capability that communicates images back to a centralprocessor to correlate those images with images captured by the cameraarray. This can give a more robust image of the environment for modelingusing multiple perspectives. Furthermore, some imaging devices may be ofsuitable resolution or have a suitable perspective on the scene to pickup information stored in quick response (QR) codes or barcodes. This canbe helpful in identifying specific objects not manually registered withthe system. In some embodiments, the camera may be mounted on theRobotic Arm 105A.

Although, as discussed herein, the majority of tracking and/ornavigation techniques utilize image-based tracking systems (e.g., IRtracking systems, video or image based tracking systems, etc.). However,electromagnetic (EM) based tracking systems are becoming more common fora variety of reasons. For example, implantation of standard opticaltrackers requires tissue resection (e.g., down to the cortex) as well assubsequent drilling and driving of cortical pins. Additionally, becauseoptical trackers require a direct line of sight with a tracking system,the placement of such trackers may need to be far from the surgical siteto ensure they do not restrict the movement of a surgeon or medicalprofessional.

Generally, EM based tracking devices include one or more wire coils anda reference field generator. The one or more wire coils may be energized(e.g., via a wired or wireless power supply). Once energized, the coilcreates an electromagnetic field that can be detected and measured(e.g., by the reference field generator or an additional device) in amanner that allows for the location and orientation of the one or morewire coils to be determined. As should be understood by someone ofordinary skill in the art, a single coil, such as is shown in FIG. 2 ,is limited to detecting five (5) total degrees-of-freedom (DOF). Forexample, sensor 200 may be able to track/determine movement in the X, Y,or Z direction, as well as rotation around the Y-axis 202 or Z-axis 201.However, because of the electromagnetic properties of a coil, it is notpossible to properly track rotational movement around the X axis.

Accordingly, in most electromagnetic tracking applications, a three coilsystem, such as that shown in FIG. 3A is used to enable tracking in allsix degrees of freedom that are possible for a rigid body moving in athree-dimensional space (i.e., forward/backward 310, up/down 320,left/right 330, roll 340, pitch 350, and yaw 360). However, theinclusion of two additional coils and the 90° offset angles at whichthey are positioned may require the tracking device to be much larger.Alternatively, as one of skill in the art would know, less than threefull coils may be used to track all 6DOF. In some EM based trackingdevices, two coils may be affixed to each other, such as is shown inFIG. 3B. Because the two coils 301B and 302B are rigidly affixed to eachother, not perfectly parallel, and have locations that are knownrelative to each other, it is possible to determine the sixth degree offreedom 303B with this arrangement.

Although the use of two affixed coils (e.g., 301B and 302B) allows forEM based tracking in 6DOF, the sensor device is substantially larger indiameter than a single coil because of the additional coil. Thus, thepractical application of using an EM based tracking system in a surgicalenvironment may require tissue resection and drilling of a portion ofthe patient bone to allow for insertion of a EM tracker. Alternatively,in some embodiments, it may be possible to implant/insert a single coil,or 5DOF EM tracking device, into a patient bone using only a pin (e.g.,without the need to drill or carve out substantial bone).

Thus, as described herein, a solution is needed for which the use of anEM tracking system can be restricted to devices small enough to beinserted/embedded using a small diameter needle or pin (i.e., withoutthe need to create a new incision or large diameter opening in thebone). Accordingly, in some embodiments, a second 5DOF sensor, which isnot attached to the first, and thus has a small diameter, may be used totrack all 6DOF. Referring now to FIG. 3C, in some embodiments, two 5DOFEM sensors (e.g., 301C and 302C) may be inserted into the patient (e.g.,in a patient bone) at different locations and with different angularorientations (e.g., angle 303C is non-zero).

Referring now to FIG. 4 , an example embodiment is shown in which afirst 5DOF EM sensor 401 and a second 5DOF EM sensor 402 are insertedinto the patient bone 403 using a standard hollow needle 405 that istypical in most OR(s). In a further embodiment, the first sensor 401 andthe second sensor 402 may have an angle offset of “α” 404. In someembodiments, it may be necessary for the offset angle “α” 404 to begreater than a predetermined value (e.g., a minimum angle of 0.50°,0.75°, etc.). This minimum value may, in some embodiments, be determinedby the CASS and provided to the surgeon or medical professional duringthe surgical plan. In some embodiments, a minimum value may be based onone or more factors, such as, for example, the orientation accuracy ofthe tracking system, a distance between the first and second EM sensors.The location of the field generator, a location of the field detector, atype of EM sensor, a quality of the EM sensor, patient anatomy, and thelike.

Accordingly, as discussed herein, in some embodiments, a pin/needle(e.g., a cannulated mounting needle, etc.) may be used to insert one ormore EM sensors. Generally, the pin/needle would be a disposablecomponent, while the sensors themselves may be reusable. However, itshould be understood that this is only one potential system, and thatvarious other systems may be used in which the pin/needle and/or EMsensors are independently disposable or reusable. In a furtherembodiment, the EM sensors may be affixed to the mounting needle/pin(e.g., using a luer-lock fitting or the like), which can allow for quickassembly and disassembly. In additional embodiments, the EM sensors mayutilize an alternative sleeve and/or anchor system that allows forminimally invasive placement of the sensors.

In another embodiment, the above systems may allow for a multi-sensornavigation system that can detect and correct for field distortions thatplague electromagnetic tracking systems. It should be understood thatfield distortions may result from movement of any ferromagneticmaterials within the reference field. Thus, as one of ordinary skill inthe art would know, a typical OR has a large number of devices (e.g., anoperating table, LCD displays, lighting equipment, imaging systems,surgical instruments, etc.) that may cause interference. Furthermore,field distortions are notoriously difficult to detect. The use ofmultiple EM sensors enables the system to detect field distortionsaccurately, and/or to warn a user that the current position measurementsmay not be accurate. Because the sensors are rigidly fixed to the bonyanatomy (e.g., via the pin/needle), relative measurement of sensorpositions (X, Y, Z) may be used to detect field distortions. By way ofnon-limiting example, in some embodiments, after the EM sensors arefixed to the bone, the relative distance between the two sensors isknown and should remain constant. Thus, any change in this distancecould indicate the presence of a field distortion.

In some embodiments, specific objects can be manually registered by asurgeon with the system preoperatively or intraoperatively. For example,by interacting with a user interface, a surgeon may identify thestarting location for a tool or a bone structure. By tracking fiducialmarks associated with that tool or bone structure, or by using otherconventional image tracking modalities, a processor may track that toolor bone as it moves through the environment in a three-dimensionalmodel.

In some embodiments, certain markers, such as fiducial marks thatidentify individuals, important tools, or bones in the theater mayinclude passive or active identifiers that can be picked up by a cameraor camera array associated with the tracking system. For example, an IRLED can flash a pattern that conveys a unique identifier to the sourceof that pattern, providing a dynamic identification mark. Similarly, oneor two dimensional optical codes (barcode, QR code, etc.) can be affixedto objects in the theater to provide passive identification that canoccur based on image analysis. If these codes are placed asymmetricallyon an object, they also can be used to determine an orientation of anobject by comparing the location of the identifier with the extents ofan object in an image. For example, a QR code may be placed in a cornerof a tool tray, allowing the orientation and identity of that tray to betracked. Other tracking modalities are explained throughout. Forexample, in some embodiments, augmented reality headsets can be worn bysurgeons and other staff to provide additional camera angles andtracking capabilities.

In addition to optical tracking, certain features of objects can betracked by registering physical properties of the object and associatingthem with objects that can be tracked, such as fiducial marks fixed to atool or bone. For example, a surgeon may perform a manual registrationprocess whereby a tracked tool and a tracked bone can be manipulatedrelative to one another. By impinging the tip of the tool against thesurface of the bone, a three-dimensional surface can be mapped for thatbone that is associated with a position and orientation relative to theframe of reference of that fiducial mark. By optically tracking theposition and orientation (pose) of the fiducial mark associated withthat bone, a model of that surface can be tracked with an environmentthrough extrapolation.

The registration process that registers the CASS 100 to the relevantanatomy of the patient also can involve the use of anatomical landmarks,such as landmarks on a bone or cartilage. For example, the CASS 100 caninclude a 3D model of the relevant bone or joint and the surgeon canintraoperatively collect data regarding the location of bony landmarkson the patient’s actual bone using a probe that is connected to theCASS. Bony landmarks can include, for example, the medial malleolus andlateral malleolus, the ends of the proximal femur and distal tibia, andthe center of the hip joint. The CASS 100 can compare and register thelocation data of bony landmarks collected by the surgeon with the probewith the location data of the same landmarks in the 3D model.Alternatively, the CASS 100 can construct a 3D model of the bone orjoint without pre-operative image data by using location data of bonylandmarks and the bone surface that are collected by the surgeon using aCASS probe or other means. The registration process also can includedetermining various axes of a joint. For example, for a TKA the surgeoncan use the CASS 100 to determine the anatomical and mechanical axes ofthe femur and tibia. The surgeon and the CASS 100 can identify thecenter of the hip joint by moving the patient’s leg in a spiraldirection (i.e., circumduction) so the CASS can determine where thecenter of the hip joint is located.

A Tissue Navigation System 120 (not shown in FIG. 1 ) provides thesurgeon with intraoperative, real-time visualization for the patient’sbone, cartilage, muscle, nervous, and/or vascular tissues surroundingthe surgical area. Examples of systems that may be employed for tissuenavigation include fluorescent imaging systems and ultrasound systems.

The Display 125 provides graphical user interfaces (GUIs) that displayimages collected by the Tissue Navigation System 120 as well otherinformation relevant to the surgery. For example, in one embodiment, theDisplay 125 overlays image information collected from various modalities(e.g., CT, MRI, X-ray, fluorescent, ultrasound, etc.) collectedpre-operatively or intra-operatively to give the surgeon various viewsof the patient’s anatomy as well as real-time conditions. The Display125 may include, for example, one or more computer monitors. As analternative or supplement to the Display 125, one or more members of thesurgical staff may wear an Augmented Reality (AR) Head Mounted Device(HMD). For example, in FIG. 1 the Surgeon 111 is wearing an AR HMD 155that may, for example, overlay pre-operative image data on the patientor provide surgical planning suggestions. Various example uses of the ARHMD 155 in surgical procedures are detailed in the sections that follow.

Surgical Computer 150 provides control instructions to variouscomponents of the CASS 100, collects data from those components, andprovides general processing for various data needed during surgery. Insome embodiments, the Surgical Computer 150 is a general purposecomputer. In other embodiments, the Surgical Computer 150 may be aparallel computing platform that uses multiple central processing units(CPUs) or graphics processing units (GPU) to perform processing. In someembodiments, the Surgical Computer 150 is connected to a remote serverover one or more computer networks (e.g., the Internet). The remoteserver can be used, for example, for storage of data or execution ofcomputationally intensive processing tasks.

Various techniques generally known in the art can be used for connectingthe Surgical Computer 150 to the other components of the CASS 100.Moreover, the computers can connect to the Surgical Computer 150 using amix of technologies. For example, the End Effector 105B may connect tothe Surgical Computer 150 over a wired (i.e., serial) connection. TheTracking System 115, Tissue Navigation System 120, and Display 125 cansimilarly be connected to the Surgical Computer 150 using wiredconnections. Alternatively, the Tracking System 115, Tissue NavigationSystem 120, and Display 125 may connect to the Surgical Computer 150using wireless technologies such as, without limitation, Wi-Fi,Bluetooth, Near Field Communication (NFC), or ZigBee.

Powered Impaction and Acetabular Reamer Devices

Part of the flexibility of the CASS design described above with respectto FIG. 1 is that additional or alternative devices can be added to theCASS 100 as necessary to support particular surgical procedures. Forexample, in the context of hip surgeries, the CASS 100 may include apowered impaction device. Impaction devices are designed to repeatedlyapply an impaction force that the surgeon can use to perform activitiessuch as implant alignment. For example, within a total hip arthroplasty(THA), a surgeon will often insert a prosthetic acetabular cup into theimplant host’s acetabulum using an impaction device. Although impactiondevices can be manual in nature (e.g., operated by the surgeon strikingan impactor with a mallet), powered impaction devices are generallyeasier and quicker to use in the surgical setting. Powered impactiondevices may be powered, for example, using a battery attached to thedevice. Various attachment pieces may be connected to the poweredimpaction device to allow the impaction force to be directed in variousways as needed during surgery. Also, in the context of hip surgeries,the CASS 100 may include a powered, robotically controlled end effectorto ream the acetabulum to accommodate an acetabular cup implant.

In a robotically-assisted THA, the patient’s anatomy can be registeredto the CASS 100 using CT or other image data, the identification ofanatomical landmarks, tracker arrays attached to the patient’s bones,and one or more cameras. Tracker arrays can be mounted on the iliaccrest using clamps and/or bone pins and such trackers can be mountedexternally through the skin or internally (either posterolaterally oranterolaterally) through the incision made to perform the THA. For aTHA, the CASS 100 can utilize one or more femoral cortical screwsinserted into the proximal femur as checkpoints to aid in theregistration process. The CASS 100 also can utilize one or morecheckpoint screws inserted into the pelvis as additional checkpoints toaid in the registration process. Femoral tracker arrays can be securedto or mounted in the femoral cortical screws. The CASS 100 can employsteps where the registration is verified using a probe that the surgeonprecisely places on key areas of the proximal femur and pelvisidentified for the surgeon on the display 125. Trackers can be locatedon the robotic arm 105A or end effector 105B to register the arm and/orend effector to the CASS 100. The verification step also can utilizeproximal and distal femoral checkpoints. The CASS 100 can utilize colorprompts or other prompts to inform the surgeon that the registrationprocess for the relevant bones and the robotic arm 105A or end effector105B has been verified to a certain degree of accuracy (e.g., within 1mm).

For a THA, the CASS 100 can include a broach tracking option usingfemoral arrays to allow the surgeon to intraoperatively capture thebroach position and orientation and calculate hip length and offsetvalues for the patient. Based on information provided about thepatient’s hip joint and the planned implant position and orientationafter broach tracking is completed, the surgeon can make modificationsor adjustments to the surgical plan.

For a robotically-assisted THA, the CASS 100 can include one or morepowered reamers connected or attached to a robotic arm 105A or endeffector 105B that prepares the pelvic bone to receive an acetabularimplant according to a surgical plan. The robotic arm 105A and/or endeffector 105B can inform the surgeon and/or control the power of thereamer to ensure that the acetabulum is being resected (reamed) inaccordance with the surgical plan. For example, if the surgeon attemptsto resect bone outside of the boundary of the bone to be resected inaccordance with the surgical plan, the CASS 100 can power off the reameror instruct the surgeon to power off the reamer. The CASS 100 canprovide the surgeon with an option to turn off or disengage the roboticcontrol of the reamer. The display 125 can depict the progress of thebone being resected (reamed) as compared to the surgical plan usingdifferent colors. The surgeon can view the display of the bone beingresected (reamed) to guide the reamer to complete the reaming inaccordance with the surgical plan. The CASS 100 can provide visual oraudible prompts to the surgeon to warn the surgeon that resections arebeing made that are not in accordance with the surgical plan.

Following reaming, the CASS 100 can employ a manual or powered impactorthat is attached or connected to the robotic arm 105A or end effector105B to impact trial implants and final implants into the acetabulum.The robotic arm 105A and/or end effector 105B can be used to guide theimpactor to impact the trial and final implants into the acetabulum inaccordance with the surgical plan. The CASS 100 can cause the positionand orientation of the trial and final implants vis-à-vis the bone to bedisplayed to inform the surgeon as to how the trial and final implant’sorientation and position compare to the surgical plan, and the display125 can show the implant’s position and orientation as the surgeonmanipulates the leg and hip. The CASS 100 can provide the surgeon withthe option of re-planning and redoing the reaming and implant impactionby preparing a new surgical plan if the surgeon is not satisfied withthe original implant position and orientation.

Preoperatively, the CASS 100 can develop a proposed surgical plan basedon a three dimensional model of the hip joint and other informationspecific to the patient, such as the mechanical and anatomical axes ofthe leg bones, the epicondylar axis, the femoral neck axis, thedimensions (e.g., length) of the femur and hip, the midline axis of thehip joint, the ASIS axis of the hip joint, and the location ofanatomical landmarks such as the lesser trochanter landmarks, the distallandmark, and the center of rotation of the hip joint. TheCASS-developed surgical plan can provide a recommended optimal implantsize and implant position and orientation based on the three dimensionalmodel of the hip joint and other information specific to the patient.The CASS-developed surgical plan can include proposed details on offsetvalues, inclination and anteversion values, center of rotation, cupsize, medialization values, superior-inferior fit values, femoral stemsizing and length.

For a THA, the CASS-developed surgical plan can be viewed preoperativelyand intraoperatively, and the surgeon can modify CASS-developed surgicalplan preoperatively or intraoperatively. The CASS-developed surgicalplan can display the planned resection to the hip joint and superimposethe planned implants onto the hip joint based on the planned resections.The CASS 100 can provide the surgeon with options for different surgicalworkflows that will be displayed to the surgeon based on a surgeon’spreference. For example, the surgeon can choose from different workflowsbased on the number and types of anatomical landmarks that are checkedand captured and/or the location and number of tracker arrays used inthe registration process.

According to some embodiments, a powered impaction device used with theCASS 100 may operate with a variety of different settings. In someembodiments, the surgeon adjusts settings through a manual switch orother physical mechanism on the powered impaction device. In otherembodiments, a digital interface may be used that allows setting entry,for example, via a touchscreen on the powered impaction device. Such adigital interface may allow the available settings to vary based, forexample, on the type of attachment piece connected to the powerattachment device. In some embodiments, rather than adjusting thesettings on the powered impaction device itself, the settings can bechanged through communication with a robot or other computer systemwithin the CASS 100. Such connections may be established using, forexample, a Bluetooth or Wi-Fi networking module on the powered impactiondevice. In another embodiment, the impaction device and end pieces maycontain features that allow the impaction device to be aware of what endpiece (cup impactor, broach handle, etc.) is attached with no actionrequired by the surgeon, and adjust the settings accordingly. This maybe achieved, for example, through a QR code, barcode, RFID tag, or othermethod.

Examples of the settings that may be used include cup impaction settings(e.g., single direction, specified frequency range, specified forceand/or energy range); broach impaction settings (e.g., dualdirection/oscillating at a specified frequency range, specified forceand/or energy range); femoral head impaction settings (e.g., singledirection/single blow at a specified force or energy); and stemimpaction settings (e.g., single direction at specified frequency with aspecified force or energy). Additionally, in some embodiments, thepowered impaction device includes settings related to acetabular linerimpaction (e.g., single direction/single blow at a specified force orenergy). There may be a plurality of settings for each type of linersuch as poly, ceramic, oxinium, or other materials. Furthermore, thepowered impaction device may offer settings for different bone qualitybased on preoperative testing/imaging/knowledge and/or intraoperativeassessment by surgeon. In some embodiments, the powered impactor devicemay have a dual function. For example, the powered impactor device notonly could provide reciprocating motion to provide an impact force, butalso could provide reciprocating motion for a broach or rasp.

In some embodiments, the powered impaction device includes feedbacksensors that gather data during instrument use and send data to acomputing device, such as a controller within the device or the SurgicalComputer 150. This computing device can then record the data for lateranalysis and use. Examples of the data that may be collected include,without limitation, sound waves, the predetermined resonance frequencyof each instrument, reaction force or rebound energy from patient bone,location of the device with respect to imaging (e.g., fluoro, CT,ultrasound, MRI, etc.) registered bony anatomy, and/or external straingauges on bones.

Once the data is collected, the computing device may execute one or morealgorithms in real-time or near real-time to aid the surgeon inperforming the surgical procedure. For example, in some embodiments, thecomputing device uses the collected data to derive information such asthe proper final broach size (femur); when the stem is fully seated(femur side); or when the cup is seated (depth and/or orientation) for aTHA. Once the information is known, it may be displayed for thesurgeon’s review, or it may be used to activate haptics or otherfeedback mechanisms to guide the surgical procedure.

Additionally, the data derived from the aforementioned algorithms may beused to drive operation of the device. For example, during insertion ofa prosthetic acetabular cup with a powered impaction device, the devicemay automatically extend an impaction head (e.g., an end effector)moving the implant into the proper location, or turn the power off tothe device once the implant is fully seated. In one embodiment, thederived information may be used to automatically adjust settings forquality of bone where the powered impaction device should use less powerto mitigate femoral/acetabular/pelvic fracture or damage to surroundingtissues.

Robotic Arm

In some embodiments, the CASS 100 includes a robotic arm 105A thatserves as an interface to stabilize and hold a variety of instrumentsused during the surgical procedure. For example, in the context of a hipsurgery, these instruments may include, without limitation, retractors,a sagittal or reciprocating saw, the reamer handle, the cup impactor,the broach handle, and the stem inserter. The robotic arm 105A may havemultiple degrees of freedom (like a Spider device), and have the abilityto be locked in place (e.g., by a press of a button, voice activation, asurgeon removing a hand from the robotic arm, or other method).

In some embodiments, movement of the robotic arm 105A may be effectuatedby use of a control panel built into the robotic arm system. Forexample, a display screen may include one or more input sources, such asphysical buttons or a user interface having one or more icons, thatdirect movement of the robotic arm 105A. The surgeon or other healthcareprofessional may engage with the one or more input sources to positionthe robotic arm 105A when performing a surgical procedure.

A tool or an end effector 105B attached or integrated into a robotic arm105A may include, without limitation, a burring device, a scalpel, acutting device, a retractor, a joint tensioning device, or the like. Inembodiments in which an end effector 105B is used, the end effector maybe positioned at the end of the robotic arm 105A such that any motorcontrol operations are performed within the robotic arm system. Inembodiments in which a tool is used, the tool may be secured at a distalend of the robotic arm 105A, but motor control operation may residewithin the tool itself.

The robotic arm 105A may be motorized internally to both stabilize therobotic arm, thereby preventing it from falling and hitting the patient,surgical table, surgical staff, etc., and to allow the surgeon to movethe robotic arm without having to fully support its weight. While thesurgeon is moving the robotic arm 105A, the robotic arm may provide someresistance to prevent the robotic arm from moving too fast or having toomany degrees of freedom active at once. The position and the lock statusof the robotic arm 105A may be tracked, for example, by a controller orthe Surgical Computer 150.

In some embodiments, the robotic arm 105A can be moved by hand (e.g., bythe surgeon) or with internal motors into its ideal position andorientation for the task being performed. In some embodiments, therobotic arm 105A may be enabled to operate in a “free” mode that allowsthe surgeon to position the arm into a desired position without beingrestricted. While in the free mode, the position and orientation of therobotic arm 105A may still be tracked as described above. In oneembodiment, certain degrees of freedom can be selectively released uponinput from user (e.g., surgeon) during specified portions of thesurgical plan tracked by the Surgical Computer 150. Designs in which arobotic arm 105A is internally powered through hydraulics or motors orprovides resistance to external manual motion through similar means canbe described as powered robotic arms, while arms that are manuallymanipulated without power feedback, but which may be manually orautomatically locked in place, may be described as passive robotic arms.

A robotic arm 105A or end effector 105B can include a trigger or othermeans to control the power of a saw or drill. Engagement of the triggeror other means by the surgeon can cause the robotic arm 105A or endeffector 105B to transition from a motorized alignment mode to a modewhere the saw or drill is engaged and powered on. Additionally, the CASS100 can include a foot pedal (not shown) that causes the system toperform certain functions when activated. For example, the surgeon canactivate the foot pedal to instruct the CASS 100 to place the roboticarm 105A or end effector 105B in an automatic mode that brings therobotic arm or end effector into the proper position with respect to thepatient’s anatomy in order to perform the necessary resections. The CASS100 also can place the robotic arm 105A or end effector 105B in acollaborative mode that allows the surgeon to manually manipulate andposition the robotic arm or end effector into a particular location. Thecollaborative mode can be configured to allow the surgeon to move therobotic arm 105A or end effector 105B medially or laterally, whilerestricting movement in other directions. As discussed, the robotic arm105A or end effector 105B can include a cutting device (saw, drill, andburr) or a cutting guide or jig 105D that will guide a cutting device.In other embodiments, movement of the robotic arm 105A or roboticallycontrolled end effector 105B can be controlled entirely by the CASS 100without any, or with only minimal, assistance or input from a surgeon orother medical professional. In still other embodiments, the movement ofthe robotic arm 105A or robotically controlled end effector 105B can becontrolled remotely by a surgeon or other medical professional using acontrol mechanism separate from the robotic arm or roboticallycontrolled end effector device, for example using a joystick orinteractive monitor or display control device.

The examples below describe uses of the robotic device in the context ofa hip surgery; however, it should be understood that the robotic arm mayhave other applications for surgical procedures involving knees,shoulders, etc. One example of use of a robotic arm in the context offorming an anterior cruciate ligament (ACL) graft tunnel is described inWIPO Publication No. WO 2020/047051, filed Aug. 28, 2019, entitled“Robotic Assisted Ligament Graft Placement and Tensioning,” the entiretyof which is incorporated herein by reference.

A robotic arm 105A may be used for holding the retractor. For example inone embodiment, the robotic arm 105A may be moved into the desiredposition by the surgeon. At that point, the robotic arm 105A may lockinto place. In some embodiments, the robotic arm 105A is provided withdata regarding the patient’s position, such that if the patient moves,the robotic arm can adjust the retractor position accordingly. In someembodiments, multiple robotic arms may be used, thereby allowingmultiple retractors to be held or for more than one activity to beperformed simultaneously (e.g., retractor holding & reaming).

The robotic arm 105A may also be used to help stabilize the surgeon’shand while making a femoral neck cut. In this application, control ofthe robotic arm 105A may impose certain restrictions to prevent softtissue damage from occurring. For example, in one embodiment, theSurgical Computer 150 tracks the position of the robotic arm 105A as itoperates. If the tracked location approaches an area where tissue damageis predicted, a command may be sent to the robotic arm 105A causing itto stop. Alternatively, where the robotic arm 105A is automaticallycontrolled by the Surgical Computer 150, the Surgical Computer mayensure that the robotic arm is not provided with any instructions thatcause it to enter areas where soft tissue damage is likely to occur. TheSurgical Computer 150 may impose certain restrictions on the surgeon toprevent the surgeon from reaming too far into the medial wall of theacetabulum or reaming at an incorrect angle or orientation.

In some embodiments, the robotic arm 105A may be used to hold a cupimpactor at a desired angle or orientation during cup impaction. Whenthe final position has been achieved, the robotic arm 105A may preventany further seating to prevent damage to the pelvis.

The surgeon may use the robotic arm 105A to position the broach handleat the desired position and allow the surgeon to impact the broach intothe femoral canal at the desired orientation. In some embodiments, oncethe Surgical Computer 150 receives feedback that the broach is fullyseated, the robotic arm 105A may restrict the handle to prevent furtheradvancement of the broach.

The robotic arm 105A may also be used for resurfacing applications. Forexample, the robotic arm 105A may stabilize the surgeon while usingtraditional instrumentation and provide certain restrictions orlimitations to allow for proper placement of implant components (e.g.,guide wire placement, chamfer cutter, sleeve cutter, plan cutter, etc.).Where only a burr is employed, the robotic arm 105A may stabilize thesurgeon’s handpiece and may impose restrictions on the handpiece toprevent the surgeon from removing unintended bone in contravention ofthe surgical plan.

The robotic arm 105A may be a passive arm. As an example, the roboticarm 105A may be a CIRQ robot arm available from Brainlab AG. CIRQ is aregistered trademark of Brainlab AG, Olof-Palme-Str. 9 81829, Munchen,FED REP of GERMANY. In one particular embodiment, the robotic arm 105Ais an intelligent holding arm as disclosed in U.S. Pat. Application No.15/525,585 to Krinninger et al., U.S. Pat. Application No. 15/561,042 toNowatschin et al., U.S. Pat. Application No. 15/561,048 to Nowatschin etal., and U.S. Pat. No. 10,342,636 to Nowatschin et al., the entirecontents of each of which is herein incorporated by reference.

Surgical Procedure Data Generation and Collection

The various services that are provided by medical professionals to treata clinical condition are collectively referred to as an “episode ofcare.” For a particular surgical intervention the episode of care caninclude three phases: pre-operative, intra-operative, andpost-operative. During each phase, data is collected or generated thatcan be used to analyze the episode of care in order to understandvarious features of the procedure and identify patterns that may beused, for example, in training models to make decisions with minimalhuman intervention. The data collected over the episode of care may bestored at the Surgical Computer 150 or the Surgical Data Server 180 as acomplete dataset. Thus, for each episode of care, a dataset exists thatcomprises all of the data collectively pre-operatively about thepatient, all of the data collected or stored by the CASS 100intra-operatively, and any post-operative data provided by the patientor by a healthcare professional monitoring the patient.

As explained in further detail, the data collected during the episode ofcare may be used to enhance performance of the surgical procedure or toprovide a holistic understanding of the surgical procedure and thepatient outcomes. For example, in some embodiments, the data collectedover the episode of care may be used to generate a surgical plan. In oneembodiment, a high-level, pre-operative plan is refinedintra-operatively as data is collected during surgery. In this way, thesurgical plan can be viewed as dynamically changing in real-time or nearreal-time as new data is collected by the components of the CASS 100. Inother embodiments, pre-operative images or other input data may be usedto develop a robust plan preoperatively that is simply executed duringsurgery. In this case, the data collected by the CASS 100 during surgerymay be used to make recommendations that ensure that the surgeon stayswithin the pre-operative surgical plan. For example, if the surgeon isunsure how to achieve a certain prescribed cut or implant alignment, theSurgical Computer 150 can be queried for a recommendation. In stillother embodiments, the pre-operative and intra-operative planningapproaches can be combined such that a robust pre-operative plan can bedynamically modified, as necessary or desired, during the surgicalprocedure. In some embodiments, a biomechanics-based model of patientanatomy contributes simulation data to be considered by the CASS 100 indeveloping preoperative, intraoperative, andpost-operative/rehabilitation procedures to optimize implant performanceoutcomes for the patient.

Aside from changing the surgical procedure itself, the data gatheredduring the episode of care may be used as an input to other proceduresancillary to the surgery. For example, in some embodiments, implants canbe designed using episode of care data. Example data-driven techniquesfor designing, sizing, and fitting implants are described in U.S. Pat.Application No. 13/814,531 filed Aug. 15, 2011 and entitled “Systems andMethods for Optimizing Parameters for Orthopaedic Procedures”; U.S. Pat.Application No. 14/232,958 filed Jul. 20, 2012 and entitled “Systems andMethods for Optimizing Fit of an Implant to Anatomy”; and U.S. Pat.Application No. 12/234,444 filed Sep. 19, 2008 and entitled “OperativelyTuning Implants for Increased Performance,” the entire contents of eachof which are hereby incorporated by reference into this patentapplication.

Furthermore, the data can be used for educational, training, or researchpurposes. For example, using the network-based approach described belowin FIG. 5C, other doctors or students can remotely view surgeries ininterfaces that allow them to selectively view data as it is collectedfrom the various components of the CASS 100. After the surgicalprocedure, similar interfaces may be used to “playback” a surgery fortraining or other educational purposes, or to identify the source of anyissues or complications with the procedure.

Data acquired during the pre-operative phase generally includes allinformation collected or generated prior to the surgery. Thus, forexample, information about the patient may be acquired from a patientintake form or electronic medical record (EMR). Examples of patientinformation that may be collected include, without limitation, patientdemographics, diagnoses, medical histories, progress notes, vital signs,medical history information, allergies, and lab results. Thepre-operative data may also include images related to the anatomicalarea of interest. These images may be captured, for example, usingMagnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray,ultrasound, or any other modality known in the art. The pre-operativedata may also comprise quality of life data captured from the patient.For example, in one embodiment, pre-surgery patients use a mobileapplication (“app”) to answer questionnaires regarding their currentquality of life. In some embodiments, preoperative data used by the CASS100 includes demographic, anthropometric, cultural, or other specifictraits about a patient that can coincide with activity levels andspecific patient activities to customize the surgical plan to thepatient. For example, certain cultures or demographics may be morelikely to use a toilet that requires squatting on a daily basis.

FIGS. 5A and 5B provide examples of data that may be acquired during theintra-operative phase of an episode of care. These examples are based onthe various components of the CASS 100 described above with reference toFIG. 1 ; however, it should be understood that other types of data maybe used based on the types of equipment used during surgery and theiruse.

FIG. 5A shows examples of some of the control instructions that theSurgical Computer 150 provides to other components of the CASS 100,according to some embodiments. Note that the example of FIG. 5A assumesthat the components of the Effector Platform 105 are each controlleddirectly by the Surgical Computer 150. In embodiments where a componentis manually controlled by the Surgeon 111, instructions may be providedon the Display 125 or AR HMD 155 instructing the Surgeon 111 how to movethe component.

The various components included in the Effector Platform 105 arecontrolled by the Surgical Computer 150 providing position commands thatinstruct the component where to move within a coordinate system. In someembodiments, the Surgical Computer 150 provides the Effector Platform105 with instructions defining how to react when a component of theEffector Platform 105 deviates from a surgical plan. These commands arereferenced in FIG. 5A as “haptic” commands. For example, the EndEffector 105B may provide a force to resist movement outside of an areawhere resection is planned. Other commands that may be used by theEffector Platform 105 include vibration and audio cues.

In some embodiments, the end effectors 105B of the robotic arm 105A areoperatively coupled with cutting guide 105D. In response to ananatomical model of the surgical scene, the robotic arm 105A can movethe end effectors 105B and the cutting guide 105D into position to matchthe location of the femoral or tibial cut to be performed in accordancewith the surgical plan. This can reduce the likelihood of error,allowing the vision system and a processor utilizing that vision systemto implement the surgical plan to place a cutting guide 105D at theprecise location and orientation relative to the tibia or femur to aligna cutting slot of the cutting guide with the cut to be performedaccording to the surgical plan. Then, a surgeon can use any suitabletool, such as an oscillating or rotating saw or drill to perform the cut(or drill a hole) with perfect placement and orientation because thetool is mechanically limited by the features of the cutting guide 105D.In some embodiments, the cutting guide 105D may include one or more pinholes that are used by a surgeon to drill and screw or pin the cuttingguide into place before performing a resection of the patient tissueusing the cutting guide. This can free the robotic arm 105A or ensurethat the cutting guide 105D is fully affixed without moving relative tothe bone to be resected. For example, this procedure can be used to makethe first distal cut of the femur during a total knee arthroplasty. Insome embodiments, where the arthroplasty is a hip arthroplasty, cuttingguide 105D can be fixed to the femoral head or the acetabulum for therespective hip arthroplasty resection. It should be understood that anyarthroplasty that utilizes precise cuts can use the robotic arm 105Aand/or cutting guide 105D in this manner.

The Resection Equipment 110 is provided with a variety of commands toperform bone or tissue operations. As with the Effector Platform 105,position information may be provided to the Resection Equipment 110 tospecify where it should be located when performing resection. Othercommands provided to the Resection Equipment 110 may be dependent on thetype of resection equipment. For example, for a mechanical or ultrasonicresection tool, the commands may specify the speed and frequency of thetool. For Radiofrequency Ablation (RFA) and other laser ablation tools,the commands may specify intensity and pulse duration.

Some components of the CASS 100 do not need to be directly controlled bythe Surgical Computer 150; rather, the Surgical Computer 150 only needsto activate the component, which then executes software locallyspecifying the manner in which to collect data and provide it to theSurgical Computer 150. In the example of FIG. 5A, there are twocomponents that are operated in this manner: the Tracking System 115 andthe Tissue Navigation System 120.

The Surgical Computer 150 provides the Display 125 with anyvisualization that is needed by the Surgeon 111 during surgery. Formonitors, the Surgical Computer 150 may provide instructions fordisplaying images, GUIs, etc. using techniques known in the art. Thedisplay 125 can include various portions of the workflow of a surgicalplan. During the registration process, for example, the display 125 canshow a preoperatively constructed 3D bone model and depict the locationsof the probe as the surgeon uses the probe to collect locations ofanatomical landmarks on the patient. The display 125 can includeinformation about the surgical target area. For example, in connectionwith a TKA, the display 125 can depict the mechanical and anatomicalaxes of the femur and tibia. The display 125 can depict varus and valgusangles for the knee joint based on a surgical plan, and the CASS 100 candepict how such angles will be affected if contemplated revisions to thesurgical plan are made. Accordingly, the display 125 is an interactiveinterface that can dynamically update and display how changes to thesurgical plan would impact the procedure and the final position andorientation of implants installed on bone.

As the workflow progresses to preparation of bone cuts or resections,the display 125 can depict the planned or recommended bone cuts beforeany cuts are performed. The surgeon 111 can manipulate the image displayto provide different anatomical perspectives of the target area and canhave the option to alter or revise the planned bone cuts based onintraoperative evaluation of the patient. The display 125 can depict howthe chosen implants would be installed on the bone if the planned bonecuts are performed. If the surgeon 111 choses to change the previouslyplanned bone cuts, the display 125 can depict how the revised bone cutswould change the position and orientation of the implant when installedon the bone.

The display 125 can provide the surgeon 111 with a variety of data andinformation about the patient, the planned surgical intervention, andthe implants. Various patient-specific information can be displayed,including real-time data concerning the patient’s health such as heartrate, blood pressure, etc. The display 125 also can include informationabout the anatomy of the surgical target region including the locationof landmarks, the current state of the anatomy (e.g., whether anyresections have been made, the depth and angles of planned and executedbone cuts), and future states of the anatomy as the surgical planprogresses. The display 125 also can provide or depict additionalinformation about the surgical target region. For a TKA, the display 125can provide information about the gaps (e.g., gap balancing) between thefemur and tibia and how such gaps will change if the planned surgicalplan is carried out. For a TKA, the display 125 can provide additionalrelevant information about the knee joint such as data about the joint’stension (e.g., ligament laxity) and information concerning rotation andalignment of the joint. The display 125 can depict how the plannedimplants’ locations and positions will affect the patient as the kneejoint is flexed. The display 125 can depict how the use of differentimplants or the use of different sizes of the same implant will affectthe surgical plan and preview how such implants will be positioned onthe bone. The CASS 100 can provide such information for each of theplanned bone resections in a TKA or THA. In a TKA, the CASS 100 canprovide robotic control for one or more of the planned bone resections.For example, the CASS 100 can provide robotic control only for theinitial distal femur cut, and the surgeon 111 can manually perform otherresections (anterior, posterior and chamfer cuts) using conventionalmeans, such as a 4-in-1 cutting guide or jig 105D.

The display 125 can employ different colors to inform the surgeon of thestatus of the surgical plan. For example, un-resected bone can bedisplayed in a first color, resected bone can be displayed in a secondcolor, and planned resections can be displayed in a third color.Implants can be superimposed onto the bone in the display 125, andimplant colors can change or correspond to different types or sizes ofimplants.

The information and options depicted on the display 125 can varydepending on the type of surgical procedure being performed. Further,the surgeon 111 can request or select a particular surgical workflowdisplay that matches or is consistent with his or her surgical planpreferences. For example, for a surgeon 111 who typically performs thetibial cuts before the femoral cuts in a TKA, the display 125 andassociated workflow can be adapted to take this preference into account.The surgeon 111 also can preselect that certain steps be included ordeleted from the standard surgical workflow display. For example, if asurgeon 111 uses resection measurements to finalize an implant plan butdoes not analyze ligament gap balancing when finalizing the implantplan, the surgical workflow display can be organized into modules, andthe surgeon can select which modules to display and the order in whichthe modules are provided based on the surgeon’s preferences or thecircumstances of a particular surgery. Modules directed to ligament andgap balancing, for example, can include pre- and post-resectionligament/gap balancing, and the surgeon 111 can select which modules toinclude in their default surgical plan workflow depending on whetherthey perform such ligament and gap balancing before or after (or both)bone resections are performed.

For more specialized display equipment, such as AR HMDs, the SurgicalComputer 150 may provide images, text, etc. using the data formatsupported by the equipment. For example, if the Display 125 is aholography device such as the Microsoft HoloLens™ or Magic Leap One™,the Surgical Computer 150 may use the HoloLens Application ProgramInterface (API) to send commands specifying the position and content ofholograms displayed in the field of view of the Surgeon 111.

In some embodiments, one or more surgical planning models may beincorporated into the CASS 100 and used in the development of thesurgical plans provided to the surgeon 111. The term “surgical planningmodel” refers to software that simulates the biomechanics performance ofanatomy under various scenarios to determine the optimal way to performcutting and other surgical activities. For example, for knee replacementsurgeries, the surgical planning model can measure parameters forfunctional activities, such as deep knee bends, gait, etc., and selectcut locations on the knee to optimize implant placement. One example ofa surgical planning model is the LIFEMOD™ simulation software from SMITHAND NEPHEW, INC. In some embodiments, the Surgical Computer 150 includescomputing architecture that allows full execution of the surgicalplanning model during surgery (e.g., a GPU-based parallel processingenvironment). In other embodiments, the Surgical Computer 150 may beconnected over a network to a remote computer that allows suchexecution, such as a Surgical Data Server 180 (see FIG. 5C). As analternative to full execution of the surgical planning model, in someembodiments, a set of transfer functions are derived that simplify themathematical operations captured by the model into one or more predictorequations. Then, rather than execute the full simulation during surgery,the predictor equations are used. Further details on the use of transferfunctions are described in WIPO Publication No. 2020/037308, filed Aug.19, 2019, entitled “Patient Specific Surgical Method and System,” theentirety of which is incorporated herein by reference.

FIG. 5B shows examples of some of the types of data that can be providedto the Surgical Computer 150 from the various components of the CASS100. In some embodiments, the components may stream data to the SurgicalComputer 150 in real-time or near real-time during surgery. In otherembodiments, the components may queue data and send it to the SurgicalComputer 150 at set intervals (e.g., every second). Data may becommunicated using any format known in the art. Thus, in someembodiments, the components all transmit data to the Surgical Computer150 in a common format. In other embodiments, each component may use adifferent data format, and the Surgical Computer 150 is configured withone or more software applications that enable translation of the data.

In general, the Surgical Computer 150 may serve as the central pointwhere CASS data is collected. The exact content of the data will varydepending on the source. For example, each component of the EffectorPlatform 105 provides a measured position to the Surgical Computer 150.Thus, by comparing the measured position to a position originallyspecified by the Surgical Computer 150 (see FIG. 5B), the SurgicalComputer can identify deviations that take place during surgery.

The Resection Equipment 110 can send various types of data to theSurgical Computer 150 depending on the type of equipment used. Exampledata types that may be sent include the measured torque, audiosignatures, and measured displacement values. Similarly, the TrackingTechnology 115 can provide different types of data depending on thetracking methodology employed. Example tracking data types includeposition values for tracked items (e.g., anatomy, tools, etc.),ultrasound images, and surface or landmark collection points or axes.The Tissue Navigation System 120 provides the Surgical Computer 150 withanatomic locations, shapes, etc. as the system operates.

Although the Display 125 generally is used for outputting data forpresentation to the user, it may also provide data to the SurgicalComputer 150. For example, for embodiments where a monitor is used aspart of the Display 125, the Surgeon 111 may interact with a GUI toprovide inputs which are sent to the Surgical Computer 150 for furtherprocessing. For AR applications, the measured position and displacementof the HMD may be sent to the Surgical Computer 150 so that it canupdate the presented view as needed.

During the post-operative phase of the episode of care, various types ofdata can be collected to quantify the overall improvement ordeterioration in the patient’s condition as a result of the surgery. Thedata can take the form of, for example, self-reported informationreported by patients via questionnaires. For example, in the context ofa knee replacement surgery, functional status can be measured with anOxford Knee Score questionnaire, and the post-operative quality of lifecan be measured with a EQ5D-5L questionnaire. Other examples in thecontext of a hip replacement surgery may include the Oxford Hip Score,Harris Hip Score, and WOMAC (Western Ontario and McMaster UniversitiesOsteoarthritis index). Such questionnaires can be administered, forexample, by a healthcare professional directly in a clinical setting orusing a mobile app that allows the patient to respond to questionsdirectly. In some embodiments, the patient may be outfitted with one ormore wearable devices that collect data relevant to the surgery. Forexample, following a knee surgery, the patient may be outfitted with aknee brace that includes sensors that monitor knee positioning,flexibility, etc. This information can be collected and transferred tothe patient’s mobile device for review by the surgeon to evaluate theoutcome of the surgery and address any issues. In some embodiments, oneor more cameras can capture and record the motion of a patient’s bodysegments during specified activities postoperatively. This motioncapture can be compared to a biomechanics model to better understand thefunctionality of the patient’s joints and better predict progress inrecovery and identify any possible revisions that may be needed.

The post-operative stage of the episode of care can continue over theentire life of a patient. For example, in some embodiments, the SurgicalComputer 150 or other components comprising the CASS 100 can continue toreceive and collect data relevant to a surgical procedure after theprocedure has been performed. This data may include, for example,images, answers to questions, “normal” patient data (e.g., blood type,blood pressure, conditions, medications, etc.), biometric data (e.g.,gait, etc.), and objective and subjective data about specific issues(e.g., knee or hip joint pain). This data may be explicitly provided tothe Surgical Computer 150 or other CASS component by the patient or thepatient’s physician(s). Alternatively or additionally, the SurgicalComputer 150 or other CASS component can monitor the patient’s EMR andretrieve relevant information as it becomes available. This longitudinalview of the patient’s recovery allows the Surgical Computer 150 or otherCASS component to provide a more objective analysis of the patient’soutcome to measure and track success or lack of success for a givenprocedure. For example, a condition experienced by a patient long afterthe surgical procedure can be linked back to the surgery through aregression analysis of various data items collected during the episodeof care. This analysis can be further enhanced by performing theanalysis on groups of patients that had similar procedures and/or havesimilar anatomies.

In some embodiments, data is collected at a central location to providefor easier analysis and use. Data can be manually collected from variousCASS components in some instances. For example, a portable storagedevice (e.g., USB stick) can be attached to the Surgical Computer 150into order to retrieve data collected during surgery. The data can thenbe transferred, for example, via a desktop computer to the centralizedstorage. Alternatively, in some embodiments, the Surgical Computer 150is connected directly to the centralized storage via a Network 175 asshown in FIG. 5C.

FIG. 5C illustrates a “cloud-based” implementation in which the SurgicalComputer 150 is connected to a Surgical Data Server 180 via a Network175. This Network 175 may be, for example, a private intranet or theInternet. In addition to the data from the Surgical Computer 150, othersources can transfer relevant data to the Surgical Data Server 180. Theexample of FIG. 5C shows 3 additional data sources: the Patient 160,Healthcare Professional(s) 165, and an EMR Database 170. Thus, thePatient 160 can send pre-operative and post-operative data to theSurgical Data Server 180, for example, using a mobile app. TheHealthcare Professional(s) 165 includes the surgeon and his or her staffas well as any other professionals working with Patient 160 (e.g., apersonal physician, a rehabilitation specialist, etc.). It should alsobe noted that the EMR Database 170 may be used for both pre-operativeand post-operative data. For example, assuming that the Patient 160 hasgiven adequate permissions, the Surgical Data Server 180 may collect theEMR of the Patient pre-surgery. Then, the Surgical Data Server 180 maycontinue to monitor the EMR for any updates post-surgery.

At the Surgical Data Server 180, an Episode of Care Database 185 is usedto store the various data collected over a patient’s episode of care.The Episode of Care Database 185 may be implemented using any techniqueknown in the art. For example, in some embodiments, a SQL-based databasemay be used where all of the various data items are structured in amanner that allows them to be readily incorporated in two SQL’scollection of rows and columns. However, in other embodiments a No-SQLdatabase may be employed to allow for unstructured data, while providingthe ability to rapidly process and respond to queries. As is understoodin the art, the term “No-SQL” is used to define a class of data storesthat are non-relational in their design. Various types of No-SQLdatabases may generally be grouped according to their underlying datamodel. These groupings may include databases that use column-based datamodels (e.g., Cassandra), document-based data models (e.g., MongoDB),key-value based data models (e.g., Redis), and/or graph-based datamodels (e.g., Allego). Any type of No-SQL database may be used toimplement the various embodiments described herein and, in someembodiments, the different types of databases may support the Episode ofCare Database 185.

Data can be transferred between the various data sources and theSurgical Data Server 180 using any data format and transfer techniqueknown in the art. It should be noted that the architecture shown in FIG.5C allows transmission from the data source to the Surgical Data Server180, as well as retrieval of data from the Surgical Data Server 180 bythe data sources. For example, as explained in detail below, in someembodiments, the Surgical Computer 150 may use data from past surgeries,machine learning models, etc. to help guide the surgical procedure.

In some embodiments, the Surgical Computer 150 or the Surgical DataServer 180 may execute a de-identification process to ensure that datastored in the Episode of Care Database 185 meets Health InsurancePortability and Accountability Act (HIPAA) standards or otherrequirements mandated by law. HIPAA provides a list of certainidentifiers that must be removed from data during de-identification. Theaforementioned de-identification process can scan for these identifiersin data that is transferred to the Episode of Care Database 185 forstorage. For example, in one embodiment, the Surgical Computer 150executes the de-identification process just prior to initiating transferof a particular data item or set of data items to the Surgical DataServer 180. In some embodiments, a unique identifier is assigned to datafrom a particular episode of care to allow for re-identification of thedata if necessary.

Although FIGS. 5A - 5C discuss data collection in the context of asingle episode of care, it should be understood that the general conceptcan be extended to data collection from multiple episodes of care. Forexample, surgical data may be collected over an entire episode of careeach time a surgery is performed with the CASS 100 and stored at theSurgical Computer 150 or at the Surgical Data Server 180. As explainedin further detail below, a robust database of episode of care dataallows the generation of optimized values, measurements, distances, orother parameters and other recommendations related to the surgicalprocedure. In some embodiments, the various datasets are indexed in thedatabase or other storage medium in a manner that allows for rapidretrieval of relevant information during the surgical procedure. Forexample, in one embodiment, a patient-centric set of indices may be usedso that data pertaining to a particular patient or a set of patientssimilar to a particular patient can be readily extracted. This conceptcan be similarly applied to surgeons, implant characteristics, CASScomponent versions, etc.

Further details of the management of episode of care data is describedin U.S. Pat. Application No. 62/783,858 filed Dec. 21, 2018 and entitled“Methods and Systems for Providing an Episode of Care,” the entirety ofwhich is incorporated herein by reference.

Open Versus Closed Digital Ecosystems

In some embodiments, the CASS 100 is designed to operate as aself-contained or “closed” digital ecosystem. Each component of the CASS100 is specifically designed to be used in the closed ecosystem, anddata is generally not accessible to devices outside of the digitalecosystem. For example, in some embodiments, each component includessoftware or firmware that implements proprietary protocols foractivities such as communication, storage, security, etc. The concept ofa closed digital ecosystem may be desirable for a company that wants tocontrol all components of the CASS 100 to ensure that certaincompatibility, security, and reliability standards are met. For example,the CASS 100 can be designed such that a new component cannot be usedwith the CASS unless it is certified by the company.

In other embodiments, the CASS 100 is designed to operate as an “open”digital ecosystem. In these embodiments, components may be produced by avariety of different companies according to standards for activities,such as communication, storage, and security. Thus, by using thesestandards, any company can freely build an independent, compliantcomponent of the CASS platform. Data may be transferred betweencomponents using publicly available application programming interfaces(APIs) and open, shareable data formats.

To illustrate one type of recommendation that may be performed with theCASS 100, a technique for optimizing surgical parameters is disclosedbelow. The term “optimization” in this context means selection ofparameters that are optimal based on certain specified criteria. In anextreme case, optimization can refer to selecting optimal parameter(s)based on data from the entire episode of care, including anypre-operative data, the state of CASS data at a given point in time, andpost-operative goals. Moreover, optimization may be performed usinghistorical data, such as data generated during past surgeries involving,for example, the same surgeon, past patients with physicalcharacteristics similar to the current patient, or the like.

The optimized parameters may depend on the portion of the patient’sanatomy to be operated on. For example, for knee surgeries, the surgicalparameters may include positioning information for the femoral andtibial component including, without limitation, rotational alignment(e.g., varus/valgus rotation, external rotation, flexion rotation forthe femoral component, posterior slope of the tibial component),resection depths (e.g., varus knee, valgus knee), and implant type, sizeand position. The positioning information may further include surgicalparameters for the combined implant, such as overall limb alignment,combined tibiofemoral hyperextension, and combined tibiofemoralresection. Additional examples of parameters that could be optimized fora given TKA femoral implant by the CASS 100 include the following:

Parameter Reference Exemplary Recommendation (s) Size Posterior Thelargest sized implant that does not overhang medial/lateral bone edgesor overhang the anterior femur. A size that does not result inoverstuffing the patella femoral joint Implant Position -Medial LateralMedial/lateral cortical bone edges Center the implant evenly between themedial/lateral cortical bone edges Resection Depth -Varus Knee Distaland posterior lateral 6 mm of bone Resection Depth -Valgus Knee Distaland posterior medial 7 mm of bone Rotation -Varus/Valgus Mechanical Axis1° varus Rotation - External Transepicondylar Axis 1° external from thetransepicondylar axis Rotation - Flexion Mechanical Axis 3° flexed

Additional examples of parameters that could be optimized for a givenTKA tibial implant by the CASS 100 include the following:

Parameter Reference Exemplary Recommendation (s) Size Posterior Thelargest sized implant that does not overhang the medial, lateral,anterior, and posterior tibial edges Implant Position Medial/lateral andCenter the implant evenly between the anterior/posterior cortical boneedges medial/lateral and anterior/posterior cortical bone edgesResection Depth -Varus Knee Lateral/Medial 4 mm of bone Resection Depth-Valgus Knee Lateral/Medial 5 mm of bone Rotation -Varus/ValgusMechanical Axis 1° valgus Rotation - External Tibial Anterior PosteriorAxis 1° external from the tibial anterior paxis Posterior SlopeMechanical Axis 3° posterior slope

For hip surgeries, the surgical parameters may comprise femoral neckresection location and angle, cup inclination angle, cup anteversionangle, cup depth, femoral stem design, femoral stem size, fit of thefemoral stem within the canal, femoral offset, leg length, and femoralversion of the implant.

Shoulder parameters may include, without limitation, humeral resectiondepth/angle, humeral stem version, humeral offset, glenoid version andinclination, as well as reverse shoulder parameters such as humeralresection depth/angle, humeral stem version, Glenoid tilt/version,glenosphere orientation, glenosphere offset and offset direction.

Various conventional techniques exist for optimizing surgicalparameters. However, these techniques are typically computationallyintensive and, thus, parameters often need to be determinedpre-operatively. As a result, the surgeon is limited in his or herability to make modifications to optimized parameters based on issuesthat may arise during surgery. Moreover, conventional optimizationtechniques typically operate in a “black box” manner with little or noexplanation regarding recommended parameter values. Thus, if the surgeondecides to deviate from a recommended parameter value, the surgeontypically does so without a full understanding of the effect of thatdeviation on the rest of the surgical workflow, or the impact of thedeviation on the patient’s post-surgery quality of life.

Operative Patient Care System

The general concepts of optimization may be extended to the entireepisode of care using an Operative Patient Care System 620 that uses thesurgical data, and other data from the Patient 605 and HealthcareProfessionals 630 to optimize outcomes and patient satisfaction asdepicted in FIG. 6 .

Conventionally, pre-operative diagnosis, pre-operative surgicalplanning, intra-operative execution of a prescribed plan, andpost-operative management of total joint arthroplasty are based onindividual experience, published literature, and training knowledgebases of surgeons (ultimately, tribal knowledge of individual surgeonsand their ‘network’ of peers and journal publications) and their nativeability to make accurate intra-operative tactile discernment of“balance” and accurate manual execution of planar resections usingguides and visual cues. This existing knowledge base and execution islimited with respect to the outcomes optimization offered to patientsneeding care. For example, limits exist with respect to accuratelydiagnosing a patient to the proper, least-invasive prescribed care;aligning dynamic patient, healthcare economic, and surgeon preferenceswith patient-desired outcomes; executing a surgical plan resulting inproper bone alignment and balance, etc.; and receiving data fromdisconnected sources having different biases that are difficult toreconcile into a holistic patient framework. Accordingly, a data-driventool that more accurately models anatomical response and guides thesurgical plan can improve the existing approach.

The Operative Patient Care System 620 is designed to utilize patientspecific data, surgeon data, healthcare facility data, and historicaloutcome data to develop an algorithm that suggests or recommends anoptimal overall treatment plan for the patient’s entire episode of care(preoperative, operative, and postoperative) based on a desired clinicaloutcome. For example, in one embodiment, the Operative Patient CareSystem 620 tracks adherence to the suggested or recommended plan, andadapts the plan based on patient/care provider performance. Once thesurgical treatment plan is complete, collected data is logged by theOperative Patient Care System 620 in a historical database. Thisdatabase is accessible for future patients and the development of futuretreatment plans. In addition to utilizing statistical and mathematicalmodels, simulation tools (e.g., LIFEMOD@) can be used to simulateoutcomes, alignment, kinematics, etc. based on a preliminary or proposedsurgical plan, and reconfigure the preliminary or proposed plan toachieve desired or optimal results according to a patient’s profile or asurgeon’s preferences. The Operative Patient Care System 620 ensuresthat each patient is receiving personalized surgical and rehabilitativecare, thereby improving the chance of successful clinical outcomes andlessening the economic burden on the facility associated with near-termrevision.

In some embodiments, the Operative Patient Care System 620 employs adata collecting and management method to provide a detailed surgicalcase plan with distinct steps that are monitored and/or executed using aCASS 100. The performance of the user(s) is calculated at the completionof each step and can be used to suggest changes to the subsequent stepsof the case plan. Case plan generation relies on a series of input datathat is stored on a local or cloud-storage database. Input data can berelated to both the current patient undergoing treatment and historicaldata from patients who have received similar treatment(s).

A Patient 605 provides inputs such as Current Patient Data 610 andHistorical Patient Data 615 to the Operative Patient Care System 620.Various methods generally known in the art may be used to gather suchinputs from the Patient 605. For example, in some embodiments, thePatient 605 fills out a paper or digital survey that is parsed by theOperative Patient Care System 620 to extract patient data. In otherembodiments, the Operative Patient Care System 620 may extract patientdata from existing information sources, such as electronic medicalrecords (EMRs), health history files, and payer/provider historicalfiles. In still other embodiments, the Operative Patient Care System 620may provide an application program interface (API) that allows theexternal data source to push data to the Operative Patient Care System.For example, the Patient 605 may have a mobile phone, wearable device,or other mobile device that collects data (e.g., heart rate, pain ordiscomfort levels, exercise or activity levels, or patient-submittedresponses to the patient’s adherence with any number of pre-operativeplan criteria or conditions) and provides that data to the OperativePatient Care System 620. Similarly, the Patient 605 may have a digitalapplication on his or her mobile or wearable device that enables data tobe collected and transmitted to the Operative Patient Care System 620.

Current Patient Data 610 can include, but is not limited to, activitylevel, preexisting conditions, comorbidities, prehab performance, healthand fitness level, pre-operative expectation level (relating tohospital, surgery, and recovery), a Metropolitan Statistical Area (MSA)driven score, genetic background, prior injuries (sports, trauma, etc.),previous joint arthroplasty, previous trauma procedures, previous sportsmedicine procedures, treatment of the contralateral joint or limb, gaitor biomechanical information (back and ankle issues), levels of pain ordiscomfort, care infrastructure information (payer coverage type, homehealth care infrastructure level, etc.), and an indication of theexpected ideal outcome of the procedure.

Historical Patient Data 615 can include, but is not limited to, activitylevel, preexisting conditions, comorbidities, prehab performance, healthand fitness level, pre-operative expectation level (relating tohospital, surgery, and recovery), a MSA driven score, geneticbackground, prior injuries (sports, trauma, etc.), previous jointarthroplasty, previous trauma procedures, previous sports medicineprocedures, treatment of the contralateral joint or limb, gait orbiomechanical information (back and ankle issues), levels or pain ordiscomfort, care infrastructure information (payer coverage type, homehealth care infrastructure level, etc.), expected ideal outcome of theprocedure, actual outcome of the procedure (patient reported outcomes[PROs], survivorship of implants, pain levels, activity levels, etc.),sizes of implants used, position/orientation/alignment of implants used,soft-tissue balance achieved, etc.

Healthcare Professional(s) 630 conducting the procedure or treatment mayprovide various types of data 625 to the Operative Patient Care System620. This Healthcare Professional Data 625 may include, for example, adescription of a known or preferred surgical technique (e.g., CruciateRetaining (CR) vs Posterior Stabilized (PS), up- vs downsizing,tourniquet vs tourniquet-less, femoral stem style, preferred approachfor THA, etc.), the level of training of the Healthcare Professional(s)630 (e.g., years in practice, fellowship trained, where they trained,whose techniques they emulate), previous success level includinghistorical data (outcomes, patient satisfaction), and the expected idealoutcome with respect to range of motion, days of recovery, andsurvivorship of the device. The Healthcare Professional Data 625 can becaptured, for example, with paper or digital surveys provided to theHealthcare Professional 630, via inputs to a mobile application by theHealthcare Professional, or by extracting relevant data from EMRs. Inaddition, the CASS 100 may provide data such as profile data (e.g., aPatient Specific Knee Instrument Profile) or historical logs describinguse of the CASS during surgery.

Information pertaining to the facility where the procedure or treatmentwill be conducted may be included in the input data. This data caninclude, without limitation, the following: Ambulatory Surgery Center(ASC) vs hospital, facility trauma level, Comprehensive Care for JointReplacement Program (CJR) or bundle candidacy, a MSA driven score,community vs metro, academic vs non-academic, postoperative networkaccess (Skilled Nursing Facility [SNF] only, Home Health, etc.),availability of medical professionals, implant availability, andavailability of surgical equipment.

These facility inputs can be captured by, for example and withoutlimitation, Surveys (Paper/Digital), Surgery Scheduling Tools (e.g.,apps, Websites, Electronic Medical Records [EMRs], etc.), Databases ofHospital Information (on the Internet), etc. Input data relating to theassociated healthcare economy including, but not limited to, thesocioeconomic profile of the patient, the expected level ofreimbursement the patient will receive, and if the treatment is patientspecific may also be captured.

These healthcare economic inputs can be captured by, for example andwithout limitation, Surveys (Paper/Digital), Direct Payer Information,Databases of Socioeconomic status (on the Internet with zip code), etc.Finally, data derived from simulation of the procedure is captured.Simulation inputs include implant size, position, and orientation.Simulation can be conducted with custom or commercially availableanatomical modeling software programs (e.g., LIFEMOD®, AnyBody, orOpenSIM). It is noted that the data inputs described above may not beavailable for every patient, and the treatment plan will be generatedusing the data that is available.

Prior to surgery, the Patient Data 610, 615 and Healthcare ProfessionalData 625 may be captured and stored in a cloud-based or online database(e.g., the Surgical Data Server 180 shown in FIG. 5C). Informationrelevant to the procedure is supplied to a computing system via wirelessdata transfer or manually with the use of portable media storage. Thecomputing system is configured to generate a case plan for use with aCASS 100. Case plan generation will be described hereinafter. It isnoted that the system has access to historical data from previouspatients undergoing treatment, including implant size, placement, andorientation as generated by a computer-assisted, patient-specific kneeinstrument (PSKI) selection system, or automatically by the CASS 100itself. To achieve this, case log data is uploaded to the historicaldatabase by a surgical sales rep or case engineer using an onlineportal. In some embodiments, data transfer to the online database iswireless and automated.

Historical data sets from the online database are used as inputs to amachine learning model such as, for example, a recurrent neural network(RNN) or other form of artificial neural network. As is generallyunderstood in the art, an artificial neural network functions similar toa biologic neural network and is comprised of a series of nodes andconnections. The machine learning model is trained to predict one ormore values based on the input data. For the sections that follow, it isassumed that the machine learning model is trained to generate predictorequations. These predictor equations may be optimized to determine theoptimal size, position, and orientation of the implants to achieve thebest outcome or satisfaction level.

Once the procedure is complete, all patient data and available outcomedata, including the implant size, position and orientation determined bythe CASS 100, are collected and stored in the historical database. Anysubsequent calculation of the target equation via the RNN will includethe data from the previous patient in this manner, allowing forcontinuous improvement of the system.

In addition to, or as an alternative to determining implant positioning,in some embodiments, the predictor equation and associated optimizationcan be used to generate the resection planes for use with a PSKI system.When used with a PSKI system, the predictor equation computation andoptimization are completed prior to surgery. Patient anatomy isestimated using medical image data (x-ray, CT, MRI). Global optimizationof the predictor equation can provide an ideal size and position of theimplant components. Boolean intersection of the implant components andpatient anatomy is defined as the resection volume. PSKI can be producedto remove the optimized resection envelope. In this embodiment, thesurgeon cannot alter the surgical plan intraoperatively.

The surgeon may choose to alter the surgical case plan at any time priorto or during the procedure. If the surgeon elects to deviate from thesurgical case plan, the altered size, position, and/or orientation ofthe component(s) is locked, and the global optimization is refreshedbased on the new size, position, and/or orientation of the component(s)(using the techniques previously described) to find the new idealposition of the other component(s) and the corresponding resectionsneeded to be performed to achieve the newly optimized size, positionand/or orientation of the component(s). For example, if the surgeondetermines that the size, position and/or orientation of the femoralimplant in a TKA needs to be updated or modified intraoperatively, thefemoral implant position is locked relative to the anatomy, and the newoptimal position of the tibia will be calculated (via globaloptimization) considering the surgeon’s changes to the femoral implantsize, position and/or orientation. Furthermore, if the surgical systemused to implement the case plan is robotically assisted (e.g., as withNAVIO® or the MAKO Rio), bone removal and bone morphology during thesurgery can be monitored in real time. If the resections made during theprocedure deviate from the surgical plan, the subsequent placement ofadditional components may be optimized by the processor taking intoaccount the actual resections that have already been made.

FIG. 7A illustrates how the Operative Patient Care System 620 may beadapted for performing case plan matching services. In this example,data is captured relating to the current patient 610 and is compared toall or portions of a historical database of patient data and associatedoutcomes 615. For example, the surgeon may elect to compare the plan forthe current patient against a subset of the historical database. Data inthe historical database can be filtered to include, for example, onlydata sets with favorable outcomes, data sets corresponding to historicalsurgeries of patients with profiles that are the same or similar to thecurrent patient profile, data sets corresponding to a particularsurgeon, data sets corresponding to a particular element of the surgicalplan (e.g., only surgeries where a particular ligament is retained), orany other criteria selected by the surgeon or medical professional. If,for example, the current patient data matches or is correlated with thatof a previous patient who experienced a good outcome, the case plan fromthe previous patient can be accessed and adapted or adopted for use withthe current patient. The predictor equation may be used in conjunctionwith an intra-operative algorithm that identifies or determines theactions associated with the case plan. Based on the relevant and/orpreselected information from the historical database, theintra-operative algorithm determines a series of recommended actions forthe surgeon to perform. Each execution of the algorithm produces thenext action in the case plan. If the surgeon performs the action, theresults are evaluated. The results of the surgeon’s performing theaction are used to refine and update inputs to the intra-operativealgorithm for generating the next step in the case plan. Once the caseplan has been fully executed all data associated with the case plan,including any deviations performed from the recommended actions by thesurgeon, are stored in the database of historical data. In someembodiments, the system utilizes preoperative, intraoperative, orpostoperative modules in a piecewise fashion, as opposed to the entirecontinuum of care. In other words, caregivers can prescribe anypermutation or combination of treatment modules including the use of asingle module. These concepts are illustrated in FIG. 7B and can beapplied to any type of surgery utilizing the CASS 100.

Surgery Process Display

As noted above with respect to FIGS. 1 and 5A-5C, the various componentsof the CASS 100 generate detailed data records during surgery. The CASS100 can track and record various actions and activities of the surgeonduring each step of the surgery and compare actual activity to thepre-operative or intraoperative surgical plan. In some embodiments, asoftware tool may be employed to process this data into a format wherethe surgery can be effectively “played-back.” For example, in oneembodiment, one or more GUIs may be used that depict all of theinformation presented on the Display 125 during surgery. This can besupplemented with graphs and images that depict the data collected bydifferent tools. For example, a GUI that provides a visual depiction ofthe knee during tissue resection may provide the measured torque anddisplacement of the resection equipment adjacent to the visual depictionto better provide an understanding of any deviations that occurred fromthe planned resection area. The ability to review a playback of thesurgical plan or toggle between different phases of the actual surgeryvs. the surgical plan could provide benefits to the surgeon and/orsurgical staff, allowing such persons to identify any deficiencies orchallenging phases of a surgery so that they can be modified in futuresurgeries. Similarly, in academic settings, the aforementioned GUIs canbe used as a teaching tool for training future surgeons and/or surgicalstaff. Additionally, because the data set effectively records manyelements of the surgeon’s activity, it may also be used for otherreasons (e.g., legal or compliance reasons) as evidence of correct orincorrect performance of a particular surgical procedure.

Over time, as more and more surgical data is collected, a rich libraryof data may be acquired that describes surgical procedures performed forvarious types of anatomy (knee, shoulder, hip, etc.) by differentsurgeons for different patients. Moreover, information such as implanttype and dimension, patient demographics, etc. can further be used toenhance the overall dataset. Once the dataset has been established, itmay be used to train a machine learning model (e.g., RNN) to makepredictions of how surgery will proceed based on the current state ofthe CASS 100.

Training of the machine learning model can be performed as follows. Theoverall state of the CASS 100 can be sampled over a plurality of timeperiods for the duration of the surgery. The machine learning model canthen be trained to translate a current state at a first time period to afuture state at a different time period. By analyzing the entire stateof the CASS 100 rather than the individual data items, any causaleffects of interactions between different components of the CASS 100 canbe captured. In some embodiments, a plurality of machine learning modelsmay be used rather than a single model. In some embodiments, the machinelearning model may be trained not only with the state of the CASS 100,but also with patient data (e.g., captured from an EMR) and anidentification of members of the surgical staff. This allows the modelto make predictions with even greater specificity. Moreover, it allowssurgeons to selectively make predictions based only on their ownsurgical experiences if desired.

In some embodiments, predictions or recommendations made by theaforementioned machine learning models can be directly integrated intothe surgical workflow. For example, in some embodiments, the SurgicalComputer 150 may execute the machine learning model in the backgroundmaking predictions or recommendations for upcoming actions or surgicalconditions. A plurality of states can thus be predicted or recommendedfor each period. For example, the Surgical Computer 150 may predict orrecommend the state for the next 5 minutes in 30 second increments.Using this information, the surgeon can utilize a “process display” viewof the surgery that allows visualization of the future state. Forexample, FIG. 7C depicts a series of images that may be displayed to thesurgeon depicting the implant placement interface. The surgeon can cyclethrough these images, for example, by entering a particular time intothe display 125 of the CASS 100 or instructing the system to advance orrewind the display in a specific time increment using a tactile, oral,or other instruction. In one embodiment, the process display can bepresented in the upper portion of the surgeon’s field of view in the ARHMD. In some embodiments, the process display can be updated inreal-time. For example, as the surgeon moves resection tools around theplanned resection area, the process display can be updated so that thesurgeon can see how his or her actions are affecting the other factorsof the surgery.

In some embodiments, rather than simply using the current state of theCASS 100 as an input to the machine learning model, the inputs to themodel may include a planned future state. For example, the surgeon mayindicate that he or she is planning to make a particular bone resectionof the knee joint. This indication may be entered manually into theSurgical Computer 150 or the surgeon may verbally provide theindication. The Surgical Computer 150 can then produce a film stripshowing the predicted effect of the cut on the surgery. Such a filmstrip can depict over specific time increments how the surgery will beaffected, including, for example, changes in the patient’s anatomy,changes to implant position and orientation, and changes regardingsurgical intervention and instrumentation, if the contemplated course ofaction were to be performed. A surgeon or medical professional caninvoke or request this type of film strip at any point in the surgery topreview how a contemplated course of action would affect the surgicalplan if the contemplated action were to be carried out.

It should be further noted that, with a sufficiently trained machinelearning model and robotic CASS, various elements of the surgery can beautomated such that the surgeon only needs to be minimally involved, forexample, by only providing approval for various steps of the surgery.For example, robotic control using arms or other means can be graduallyintegrated into the surgical workflow over time with the surgeon slowlybecoming less and less involved with manual interaction versus robotoperation. The machine learning model in this case can learn whatrobotic commands are required to achieve certain states of theCASS-implemented plan. Eventually, the machine learning model may beused to produce a film strip or similar view or display that predictsand can preview the entire surgery from an initial state. For example,an initial state may be defined that includes the patient information,the surgical plan, implant characteristics, and surgeon preferences.Based on this information, the surgeon could preview an entire surgeryto confirm that the CASS-recommended plan meets the surgeon’sexpectations and/or requirements. Moreover, because the output of themachine learning model is the state of the CASS 100 itself, commands canbe derived to control the components of the CASS to achieve eachpredicted state. In the extreme case, the entire surgery could thus beautomated based on just the initial state information.

Using the Point Probe to Acquire High-Resolution of Key Areas During HipSurgeries

Use of the point probe is described in U.S. Patent Application No.14/955,742 entitled “Systems and Methods for Planning and PerformingImage Free Implant Revision Surgery,” the entirety of which isincorporated herein by reference. Briefly, an optically tracked pointprobe may be used to map the actual surface of the target bone thatneeds a new implant. Mapping is performed after removal of the defectiveor worn-out implant, as well as after removal of any diseased orotherwise unwanted bone. A plurality of points is collected on the bonesurfaces by brushing or scraping the entirety of the remaining bone withthe tip of the point probe. This is referred to as tracing or “painting”the bone. The collected points are used to create a three-dimensionalmodel or surface map of the bone surfaces in the computerized planningsystem. The created 3D model of the remaining bone is then used as thebasis for planning the procedure and necessary implant sizes. Analternative technique that uses X-rays to determine a 3D model isdescribed in U.S. Pat. Application No. 16/387,151, filed Apr. 17, 2019and entitled “Three-Dimensional Selective Bone Matching” and U.S. Pat.Application No. 16/789,930, filed Feb. 13, 2020 and entitled“Three-Dimensional Selective Bone Matching,” the entirety of each ofwhich is incorporated herein by reference.

For hip applications, the point probe painting can be used to acquirehigh resolution data in key areas such as the acetabular rim andacetabular fossa. This can allow a surgeon to obtain a detailed viewbefore beginning to ream. For example, in one embodiment, the pointprobe may be used to identify the floor (fossa) of the acetabulum. As iswell understood in the art, in hip surgeries, it is important to ensurethat the floor of the acetabulum is not compromised during reaming so asto avoid destruction of the medial wall. If the medial wall wereinadvertently destroyed, the surgery would require the additional stepof bone grafting. With this in mind, the information from the pointprobe can be used to provide operating guidelines to the acetabularreamer during surgical procedures. For example, the acetabular reamermay be configured to provide haptic feedback to the surgeon when he orshe reaches the floor or otherwise deviates from the surgical plan.Alternatively, the CASS 100 may automatically stop the reamer when thefloor is reached or when the reamer is within a threshold distance.

As an additional safeguard, the thickness of the area between theacetabulum and the medial wall could be estimated. For example, once theacetabular rim and acetabular fossa has been painted and registered tothe pre-operative 3D model, the thickness can readily be estimated bycomparing the location of the surface of the acetabulum to the locationof the medial wall. Using this knowledge, the CASS 100 may providealerts or other responses in the event that any surgical activity ispredicted to protrude through the acetabular wall while reaming.

The point probe may also be used to collect high resolution data ofcommon reference points used in orienting the 3D model to the patient.For example, for pelvic plane landmarks like the ASIS and the pubicsymphysis, the surgeon may use the point probe to paint the bone torepresent a true pelvic plane. Given a more complete view of theselandmarks, the registration software has more information to orient the3D model.

The point probe may also be used to collect high-resolution datadescribing the proximal femoral reference point that could be used toincrease the accuracy of implant placement. For example, therelationship between the tip of the Greater Trochanter (GT) and thecenter of the femoral head is commonly used as reference point to alignthe femoral component during hip arthroplasty. The alignment is highlydependent on proper location of the GT; thus, in some embodiments, thepoint probe is used to paint the GT to provide a high-resolution view ofthe area. Similarly, in some embodiments, it may be useful to have ahigh-resolution view of the Lesser Trochanter (LT). For example, duringhip arthroplasty, the Dorr Classification helps to select a stem thatwill maximize the ability of achieving a press-fit during surgery toprevent micromotion of femoral components post-surgery and ensureoptimal bony ingrowth. As is generated understood in the art, the DorrClassification measures the ratio between the canal width at the LT andthe canal width 10 cm below the LT. The accuracy of the classificationis highly dependent on the correct location of the relevant anatomy.Thus, it may be advantageous to paint the LT to provide ahigh-resolution view of the area.

In some embodiments, the point probe is used to paint the femoral neckto provide high-resolution data that allows the surgeon to betterunderstand where to make the neck cut. The navigation system can thenguide the surgeon as they perform the neck cut. For example, asunderstood in the art, the femoral neck angle is measured by placing oneline down the center of the femoral shaft and a second line down thecenter of the femoral neck. Thus, a high-resolution view of the femoralneck (and possibly the femoral shaft as well) would provide a moreaccurate calculation of the femoral neck angle.

High-resolution femoral head neck data also could be used for anavigated resurfacing procedure where the software/hardware aids thesurgeon in preparing the proximal femur and placing the femoralcomponent. As is generally understood in the art, during hipresurfacing, the femoral head and neck are not removed; rather, the headis trimmed and capped with a smooth metal covering. In this case, itwould be advantageous for the surgeon to paint the femoral head and capso that an accurate assessment of their respective geometries can beunderstood and used to guide trimming and placement of the femoralcomponent.

Registration of Pre-operative Data to Patient Anatomy Using the PointProbe

As noted above, in some embodiments, a 3D model is developed during thepre-operative stage based on 2D or 3D images of the anatomical area ofinterest. In such embodiments, registration between the 3D model and thesurgical site is performed prior to the surgical procedure. Theregistered 3D model may be used to track and measure the patient’sanatomy and surgical tools intraoperatively.

During the surgical procedure, landmarks are acquired to facilitateregistration of this pre-operative 3D model to the patient’s anatomy.For knee procedures, these points could comprise the femoral headcenter, distal femoral axis point, medial and lateral epicondyles,medial and lateral malleolus, proximal tibial mechanical axis point, andtibial A/P direction. For hip procedures these points could comprise theanterior superior iliac spine (ASIS), the pubic symphysis, points alongthe acetabular rim and within the hemisphere, the greater trochanter(GT), and the lesser trochanter (LT).

In a revision surgery, the surgeon may paint certain areas that containanatomical defects to allow for better visualization and navigation ofimplant insertion. These defects can be identified based on analysis ofthe pre-operative images. For example, in one embodiment, eachpre-operative image is compared to a library of images showing “healthy”anatomy (i.e., without defects). Any significant deviations between thepatient’s images and the healthy images can be flagged as a potentialdefect. Then, during surgery, the surgeon can be warned of the possibledefect via a visual alert on the display 125 of the CASS 100. Thesurgeon can then paint the area to provide further detail regarding thepotential defect to the Surgical Computer 150.

In some embodiments, the surgeon may use a non-contact method forregistration of bony anatomy intra-incision. For example, in oneembodiment, laser scanning is employed for registration. A laser stripeis projected over the anatomical area of interest and the heightvariations of the area are detected as changes in the line. Othernon-contact optical methods, such as white light interferometry orultrasound, may alternatively be used for surface height measurement orto register the anatomy. For example, ultrasound technology may bebeneficial where there is soft tissue between the registration point andthe bone being registered (e.g., ASIS, pubic symphysis in hipsurgeries), thereby providing for a more accurate definition of anatomicplanes.

Referring now to FIG. 8 , a flowchart of an exemplary method fortraining a machine learning model to facilitate automatic registrationof arthroscopic video images to preoperative models in accordance withan embodiment of this technology is illustrated. The method describedand illustrated with reference to FIG. 8 is carried out in some examplesby a computing device internal or external to the CASS 100 andoptionally communicably coupled to, but separate from, the surgicalcomputer 150 (e.g., the computing device described and illustrated belowwith reference to FIG. 12 ). In step 800 in this example, training videodata is obtained, which can be a dataset including a plurality of imageframes associated with historical video feeds captured by an arthroscopeduring surgical procedures, for example. The image frames in thetraining video data can be annotated with labels noting the pixels orareas that belong to particular anatomical structures (e.g. an outliningof the femur, anterior cruciate ligament, and/or posterior cruciateligament).

Alternatively, the image frames in the training video data can beannotated manually or automatically with image processing softwareassistance after the training video data is obtained to reflect theanatomical structure(s) depicted therein, and other methods forobtaining annotated training video data can also be used. Optionally,each set of image frames within the training video data is capturedarthroscopically during a same surgical procedure (e.g., a total kneearthroplasty) and therefore includes one or more anatomical structuresfrom a same set of anatomical structures associated with particularpatient anatomy (e.g., a knee joint) associated with the surgicalprocedure.

In step 802, at least one machine learning model is trained based on theannotated training video data. The machine learning model in someembodiments is trained to recognize anatomical structures in similararthroscopic video data, such as arthroscopic video data obtained duringa diagnostic review that is part of a same surgical procedure, asdescribed and illustrated in more detail below. Accordingly, the machinelearning model is trained based on the obtained training video data,which includes image frames annotated to identify represented anatomicalstructures, to identify those anatomical structures when the machinelearning model is deployed and subsequently applied to diagnostic videodata.

In some examples, the machine learning model, or a separate machinelearning model, is trained based on training video data depicting softtissue anatomical structures, along with preoperative images, and/orthree-dimensional (3D) anatomical models generated therefrom,corresponding to the soft tissue anatomical structures. During somesurgical procedures, the shape and position of soft tissue changes dueto hydraulic expansion of a joint or the joint being in a differentflexion or distraction state than when the preoperative imaging wascaptured, for example. With the preoperative imaging or models andcorresponding training video data obtained during a diagnostic review orintraoperatively, the machine learning model is trained in theseexamples to predict the 3D position of soft tissue anatomical structuresin a morphed state that matches the real-time arthroscopic view. Anynumber of algorithms can be used to train the machine learning model(s)including genetic, temporal difference, k-nearest neighbor,reinforcement learning, and/or expectation maximization algorithms, forexample.

In step 804, a determination is made as to whether an accuracy of thetrained machine learning model exceeds a threshold, which can beestablished automatically, empirically, and/or manually, for example. Inother words, the determination in step 804 is whether to continue thetraining or whether the machine learning model is sufficiently accurateto be deployed. The accuracy of the machine learning model can beanalyzed via a manual review and feedback loop, for example, althoughother methods of analyzing the accuracy can also be used. If theaccuracy of the machine learning model does not exceed a threshold, thenthe No branch is taken back to step 800 and additional training videodata is obtained, annotated, and/or used to further train the machinelearning model. However, if the accuracy of the machine learning modelexceeds the threshold, then the Yes branch is taken from step 804 tostep 806.

In step 806, the machine learning model is stored for subsequent use insurgical procedures, although the machine learning model can be deployedin other manners in other examples. Optionally, the machine learningmodel can be deployed to and stored on the surgical computer 150 tofacilitate intraoperative registration of anatomical structures, asexplained in detail below.

Referring now to FIG. 9 , a flowchart of an exemplary method forapplying a machine learning model to register anatomical structuresrepresented in diagnostic arthroscopic video data and preoperative 3Danatomical models in accordance with an embodiment of this technology isillustrated. In step 900 in this example, the surgical computer 150obtains preoperative images of patient anatomy (e.g., a knee joint). Thepreoperative images can be CT or MRI images, for example, although othertypes of two dimensional (2D) images can also be used in other examples.

In step 902, the surgical computer 150 generates a 3D anatomical modelfrom the preoperative images, as is known in the art. While the surgicalcomputer 150 performs steps 900-902 in this particular example, thepreoperative imaging and model generation can be performed by adifferent device in the CASS 100 or external to the CASS in otherexamples. In these examples, the 3D model can be obtained by thesurgical computer 150 at the beginning of or during a surgical procedureassociated with the patient, for example.

In step 904, the surgical computer 150 obtains diagnostic video datacaptured by an arthroscope during a diagnostic review phase of thesurgical procedure for the patient. During arthroscopic procedures, oneof the initial steps is to perform a diagnostic review of the associatedjoint(s). For example, in a knee arthroscopy, a systematic review mayinclude sequentially observing with an arthroscope the lateralcompartment including the lateral meniscus, posterolateral corner andcartilage, the central pillar including the ACL, PCL and fat body, thesuperior part of the knee including the retropatellar cartilage, femoralgroove, and suprapatellar recessus, and the medial compartment includingthe medial meniscus, medial collateral ligament, and cartilage. Imageframes within the diagnostic video data captured during the diagnosticreview phase of a surgical procedure can be obtained in step 904 andused to automatically register the patient anatomy without requiring thefixation of fiducial markers to the patient.

In particular, in step 906, the surgical computer 150 applies a storedmachine learning model to the diagnostic video data as the diagnosticvideo data is captured (e.g., in real-time and/or immediately subsequentto the capture of the diagnostic video data). The machine learning modelmay be applied to the diagnostic video data captured via the arthroscopeto identify or resolve anatomical structure(s) represented in thediagnostic video data in this particular example. In some embodiments,the machine learning model may have been generated by the surgicalcomputer 150 or another computing device as described and illustratedabove with reference to FIG. 8 , and may have been stored by thesurgical computer as explained with reference to step 806 of FIG. 8 ,for example. Other methods of generating and storing the machinelearning model will be apparent to those of ordinary skill in the art.

The technology described and illustrated herein can leverage thesystematic nature and repeatability of the diagnostic review to predictthe anatomical structures depicted in particular image frames orportions of the diagnostic video data, and thereby more effectivelyidentify and register anatomical structures within a joint. Due to thenature of standard diagnostic arthroscopy, the anatomical structures arevisualized from a variety of perspectives, which aids in the predictionand identification process because capturing more surfaces andperspectives results in a more efficient and effective registration.While the machine learning model is applied to diagnostic video data inthe examples described and illustrated herein, the machine learningmodel can be applied to intraoperative video data obtained subsequent tothe diagnostic review phase of a surgical procedure and/or a combinationof diagnostic and intraoperative video data in other examples.

In step 908, the surgical computer 150 determines whether anunregistered anatomical structure is detected as a result of theapplication of the machine learning model in step 906 to the currentdiagnostic video data obtained in step 904. In an initial iteration, noanatomical structures will have been previously registered. Accordingly,if the surgical computer 150 determines that at least one unregisteredanatomical structure is detected, then the Yes branch is taken to step910.

In step 910, the surgical computer 150 registers the anatomicalstructure in the 3D anatomical model generated in step 902 to theanatomical structure detected in the diagnostic video data. Theregistration facilitates subsequent intraoperative tracking of theanatomical structure as well as visualization of portions of theanatomical structure occluded during the surgical procedure (e.g., viaan overlay merged with intraoperative video data), among otheradvantages described and illustrated in more detail below. However, ifthe surgical computer 150 determines in step 908 that an unregisteredanatomical structure has not been detected in the current iteration,then the No branch is taken to step 912.

In step 912, the surgical computer 150 optionally determines whether thediagnostic review phase of the surgical procedure is complete. In someembodiments, this determination may be made based on a manual input froma surgeon. In some embodiments, this determination may be madeautomatically based on a sensed parameter(s) associated with theoperating environment. If the surgical computer 150 determines that thediagnostic review phase is not complete, then the No branch is takenback to step 904, and the surgical computer 150 continues to analyzediagnostic video data using the machine learning model in order toattempt to register anatomical structures.

In some examples, the surgical computer 150 attempts to register as manyanatomical structures as possible in steps 904-912 during the diagnosticreview portion of the surgical procedure. In other examples, as few as asingle anatomical structure is registered with the location and positionof one or more additional or remaining anatomical structures beingdetermined in relation to the registered anatomical structure and/or oneor more other reference frames.

For example, initial determination of anatomical landmark features couldbe selected using a probe outfitted with a tracking fiducial, whichwould enable a user to paint the surface contour onto the anatomy todetermine the 3D surface and would anchor the 3D points relative to thedetected anatomical surface. By using the machine learning model todetect anatomical structure(s), the local reference frame could beextrapolated from the relationship of the anatomical structures relativeto one another. This approach may require “painting” the surface frommultiple camera view angles if the visual field will be used to generatethe “local” reference frame.

In other examples, the contour of just a single or a few knownanatomical structures or landmarks could be determined for placement ofthe local reference frame. In such embodiments, a bone model or softtissue model could be fit to only the known or detected anatomicalstructures (e.g., the surface of the glenoid) using key anatomicallandmarks/features. In some cases, other bone or soft tissue modelfeatures may also be positioned relative to the bone or soft-tissuemodel’s anchor structure (e.g. the glenoid).

As depicted in FIG. 9 , the surgical computer 150 effectively appliesthe machine learning model to the diagnostic video data in step 906until an anatomical structure of the patient is detected or until thediagnostic review is completed. Optionally, in subsequent iterations ofstep 906, the machine learning model can be applied to any portion ofthe diagnostic video data obtained in a prior iteration of step 904along with diagnostic video data obtained in a current iteration. If thesurgical computer 150 determines in step 912 that the diagnostic reviewphase of the surgical procedure is complete, then the Yes branch istaken to step 914.

In step 914, the surgical computer 150 begins obtaining intraoperativevideo data captured by the arthroscope during the surgical procedure andsubsequent to the diagnostic review phase. By registering the anatomicalstructures in the surgical environment with those represented in the 3Danatomical model, the surgical computer 150 can track the anatomicalstructures and provide simulated projected views based on the 3Danatomical model, as described and illustrated in more detail below withreference to FIGS. 10-11 . Further advantages of such a system may alsoresult and/or be apparent to those of ordinary skill in the art.

Referring specifically to FIG. 10 , a flowchart of an exemplary methodfor tracking registered anatomical structures in intraoperative videodata and providing simulated projected views generated from preoperative3D anatomical models in accordance with an embodiment of this technologyis illustrated. In step 1000 in this example, the surgical computer 150tracks at least one registered anatomical structure during capture ofintraoperative video data. In this particular example, theintraoperative video data is captured by an arthroscope during asurgical procedure after diagnostic video data is captured during adiagnostic review phase of the surgical procedure, although theintraoperative video data can be captured at other times in otherexamples. Optionally, the surgical computer 150 can use a biologicalcompatible dye or resorbable bone anchor, for example, to serve as theground-truth for the anatomical reference frame by creating a visiblemarker distinguishable from the background anatomical structures inorder to assist with the tracking and further improve the accuracy ofthe tracking performed by the surgical computer 150.

In step 1002, the surgical computer 150 determines an orientation of thearthroscope and extracts landmark features of the anatomical structuretracked in step 1000. In some examples, the landmark features can beextracted from an image frame of the intraoperative video data capturedin step 1000 based on an application of a machine learning model, suchas the machine learning model generated as described in detail abovewith reference to FIG. 8 , for example, although other image analysistechniques and algorithms can also be used. In other examples, theanatomical features can be identified manually based on a received inputor selection on a display of the image frame output to a display device.The orientation of the arthroscope can be determined based on a trackingof the arthroscope by a tracking system within the operative environment(e.g., vid fiducial markers attached to the arthroscope), although themachine learning model can also be used to predict the arthroscopeorientation and other methods of determining the orientation of thearthroscope can also be used.

In step 1004, the surgical computer 150 generates a simulated projectedview of at least the registered anatomical structure from the 3Danatomical model generated as described earlier with reference to step902 of FIG. 9 . The simulated projected view of the registeredanatomical structure is generated in one example from the 3D anatomicalmodel based on the determined orientation of the arthroscope duringcapture of the intraoperative video data. In some examples, one or moreof a plurality of registered anatomical structures depicted in an imageframe of the intraoperative video data is best-fit to a respective boneor soft-tissue model.

Optionally, at least a portion of the simulated projected view includesone or more anatomical structures that are occluded in a field of viewof the arthroscope and/or a mixed reality headset worn by a surgeonduring the surgical procedure that may display the intraoperative videodata captured by the arthroscope. The occluded anatomical structure(s)are appropriately located, scaled, and oriented within the simulatedprojected view based on a prior registration of the occluded anatomicalstructures directly or indirectly based on the registration of anotheranatomical structure associated with the joint, for example. Theoccluded anatomical structure(s) can then be displayed via merged videowithin the mixed reality headset, as described in more detail below.

In some embodiments, the surgical computer 150 can generate and applydifferent weightings to different areas of the 3D model based on thevariability in the appearance of particular portions or anatomicalstructures in the 2D representation when viewed with the arthroscopeand/or the likelihood of the particular portions or anatomicalstructures being seen during a standard arthroscopic procedure, forexample. For example, the medial and lateral eminences of the tibialspine have a higher surface variability and could have a higher weightfor model fitting as compared to the medial or lateral plateaus, whichhave limited variability. Accordingly, the simulated projected view canbe generated to include any portion of the 3D anatomical model based onany number of parameters and requirements for a particular deployment ofthis technology and/or type of surgical procedure, for example.

According to some embodiments, at least one of the anatomical structuresrepresented in the intraoperative video data includes soft tissue. Inone or more of these embodiments, a size and position of a first portionof the soft tissue is determined from the intraoperative video data. Themachine learning model may be applied to the 3D anatomical model and thedetermined size and position of the first portion of the soft tissue togenerate a representation of a second portion of the soft tissue in amorphed state. Additionally, the simulated projected view may includethe representation of the second portion of the soft tissue in themorphed state. During surgery, the soft tissue’s shape and positionchange. The arthroscopic view with this technology may advantageouslyallow for real-time determination of the shape and position of at leastone side of these structures.

In step 1006, the surgical computer 150 scales and orients the simulatedprojected view. The simulated projected view is scaled and orientedbased on one or more of the landmark features of the anatomicalstructure extracted from the intraoperative video data in step 1002, forexample, although the simulated projected view can be scaled and/ororiented in other ways in other examples.

In step 1008, the surgical computer 150 outputs the scaled and orientedsimulated projected view to a display device. According to someembodiments, an overlay is generated that includes the scaled andoriented simulated projected view. The generated overlay is then mergedwith the intraoperative video data provided by the arthroscope based onthe registration to the visible anatomy to generate merged video data,which is output to a mixed reality headset display device worn by asurgeon.

In some examples, one or more of a position or an orientation of themixed reality headset is tracked by a tracking system of the CASS 100and via one or more fiducial markers (e.g., a QR code) attached to themixed reality headset. In these examples, the overlay can be generatedusing a field of view of the arthroscope and a known spatial and scalerelationship between the arthroscope field of view and another referenceframe of the mixed reality headset that is determined based on thetracking. Accordingly, the external mixed reality headset could overlaythe bone or soft tissue model, or a portion thereof, utilizing thearthroscope field of view as the mechanism to determine the localreference frame, which would advantageously enable the surgeon to seethe bone or soft tissue model “through the skin” utilizing thearthroscope. Additionally, anatomical structures of interest that wouldnot be directly visible by the arthroscope due to occlusion could beoverlaid onto the arthroscopic view (e.g., within the mixed realityheadset), which would allow a surgeon to view patient anatomy from aninternal, arthroscopic perspective.

In yet other examples, the generated overlay can further includeanalytical or contextual information. For example, an annotated versionof the 3D anatomical model or the preoperative image data can beobtained that identifies an anatomical point corresponding to a portionof patient anatomy or at least one landmark feature. In one or more ofthese embodiments, the generated overlay further includes an indicationof the anatomical point. In some embodiments, a separate device externalto the CASS 100 could be used to annotate the bone model, soft-tissuemodel, and/or MRI or CT preoperative image data to highlight aparticular structure and/or landmark feature, which would then map tothe bone or soft-tissue model as a specific anatomical point that isthen overlaid on the screen following the 3D anatomical modelregistration.

The analytical information included in the generated overlay can alsoinclude indications of probes or tools or associated trajectories. Inother words, a probe or tool can be placed on a specific part of a boneor soft tissue, and the trajectory of the tool can be viewed through thetissue. For example, an indication of the location at which a guidewireor drill may exit can be represented, assistance with locating aninternal tear of the meniscus can be provided, and/or a bony cyst can berepresented to facilitate avoidance. Alternatively, the surgeon canplace a tool on a specific point and view reconstructed MRI or CT sliceviews along the trajectory of the tool or perpendicular to the tool. Inanother example, a tool such as a guide can be placed in a specificlocation and orientation, and the surgical computer 150 can simulate afluoroscopy image or post-operative image showing a tunnel location thatwould result from drilling the tunnel in that location. Accordingly, theadditional analytical information can advantageously aid indecision-making with respect to placement of tunnels, anchors, sutures,or other implants.

The represented tool or tool tip can also be depicted based on asurgical plan, according to the determined stage of the surgicalprocedure (as described and illustrated in detail below with referenceto FIG. 11 ), or to facilitate optimal access to a particular portion ofpatient anatomy. Projections of tools can be portrayed in the surgeon’sfield of view output to a head-mounted display, which guides the surgeonwith how to access a joint using a specific tool. The projection candepict an optimal orientation or multiple orientations by which a tooltip can reach a certain area or structure within the joint. The toolpositions can be optimized to access difficult to reach areas (e.g., theposterior horn of the meniscus), to avoid certain high risk structures(e.g., neurovascular structures), or to provide the most direct accessto a specific area. In these examples, the tool projections within thegenerated overlays can assist the surgeon in positioning the actualtools with increased confidence.

In yet other examples, the analytical information can be based on an eyeposition of a user (e.g., a surgeon) determined or sensed via a trackingdevice within the CASS 100. In these examples, the scaled and orientedsimulated projected view can then be output to a projector displaydevice for projection onto patient skin based on the determined eyeposition, and the projected image can be changed in real-time to be fromthe perspective of the user.

In examples in which the patient anatomy includes soft tissues that areanalyzed with respect to morphing as explained in detail above, thegenerated overlay can include the soft tissue in the morphed state incontrast to the state of the soft tissue depicted in the preoperativeimaging. For example, during meniscal repair surgery, there areneurovascular bundles that a surgeon must avoid penetrating. An MRIimage may be used to depict the size and position of these anatomicalstructures, and the current standard of care is to take the MRI with theknee in extension.

However, the meniscal repair surgical procedure is performed with theknee in flexion, and the neurovascular bundle moves duringflexion-extension. With this technology, the 3D anatomical modeldepicting soft tissue can advantageously be adjusted using machinelearning to allow surgeons to visualize various sides and aspects of thesoft tissue in the intraoperative morphed state. While some examples ofanalytical information included in generated overlays and other displaysare described and illustrated herein, other types of analyticalinformation can also be used in other exemplary deployments of thedisclosed technology.

Referring now to FIG. 11 , a flowchart of an exemplary method forgenerating merged video data based on registration of anatomicalstructures and including overlays generated to include analyticalinformation according to a state of a surgical procedure in accordancewith an embodiment of this technology is illustrated. In step 1100 inthis example, the surgical computer 150 obtains a surgical plan for asurgical procedure, such as a knee or hip arthroplasty, for example. Thesurgical plan can define steps to be performed automatically by the CASS100 in order to carry out the surgical procedure, including automatedmovements of surgical tools that facilitate resection and implantation,for example.

In step 1102, the surgical computer 150 determines a state of thesurgical procedure based on one or more detected anatomical structuresin intraoperative video data, such as intraoperative video data capturedby an arthroscope as described above with reference to step 1000 of FIG.10 . As explained above with reference to FIG. 10 , during the course ofa surgical procedure, the disclosed technology can use a machinelearning model to detect anatomy represented in a current referenceframe within intraoperative video data. By linking the detectedanatomical structures and, optionally, tools or other sensed objectswithin the operating environment, to the overall surgical procedureworkflow defined in the surgical plan, the surgical computer 150 candetermine the state of surgery. Based on the detected state of asurgical procedure, an overlay with surgical guidance can be generatedat the appropriate time in the surgery and output to a display device.For example, during an anterior cruciate ligament (ACL) reconstruction,the surgical plan for tunnel placement could be visually overlaidautomatically on a display screen or head-mounted display once thesurgical computer detects that the ACL has been resected and thedrilling phase of the procedure has commenced.

In step 1104, the surgical computer 150 determines whether an overlay isapplicable for the particular surgical procedure and at the current timebased on the state of the surgical procedure determined in step 1102.The determination in step 1104 can be automatic based on criteriaestablished preoperatively and, optionally, defined in the surgicalplan. Alternatively, the determination in step 1104 can be made inresponse to a manual input indicating a request for guidanceinformation, for example, and other methods for determining whether anoverlay is applicable in a current iteration can also be used. If thesurgical computer 150 determines that an overlay is not currentlyapplicable, then the No branch is taken back to step 1102, and thesurgical computer continues to obtain and analyze intraoperative videodata to detect anatomical structures and determine a state of thesurgical procedure. However, if the surgical computer 150 determinesthat an overlay is currently applicable, then the Yes branch is taken tostep 1106.

In step 1106, the surgical computer 150 generates an overlay withanalytical information determined from the surgical plan and/or nextstructure visual and/or textual guidance. In some examples, the surgicalcomputer 150 generates an overlay to include a simulated projected viewof one or more tracked and registered anatomical structures, asdescribed above with reference to steps 1002-1006 of FIG. 10 , alongwith analytical and/or guidance information extracted from the obtainedsurgical plan. The guidance information can include textual directionsassociated with a current task in the surgical procedure or a visualindication of an anatomical structure corresponding to a subsequent taskin the surgical procedure, for example.

Accordingly, utilizing the workflow recognition of step 1102, an overlaycan be generated that directs the surgeon toward the next structure tobe viewed. The guidance information could be an arrow added to theoverlay that points radially on the arthroscopic view towards thedirection of the next structure in the initial diagnostic reviewchecklist for a joint or an audible prompt. In other examples, theguidance information is provided for a significant portion or all of asurgical procedure, particularly for procedures that are new for theuser or more complex than a typical case. In these examples, thesurgical computer 150 could provide procedural steps, which include bothoverlays on the screen highlighting the structures to be viewed orworked on next as well as onscreen directions describing the action tobe performed. In this way, the surgical computer 150 can teach surgeonsor provide additional confidence with particular surgical procedures.

In step 1108, the surgical computer 150 outputs merged video dataincluding the intraoperative video data and the generated overlay. Insome examples, the overlay is merged at a particular location based onthe registration of one or more anatomical structures depicted in theoverlay and the intraoperative video data, as described in more detailabove. The merged video data can be output to a mixed reality headsetworn by a surgeon, for example.

Optionally, projections of tools can be included in the generatedoverlay in the surgeon’s field of view and can depict an optimal ormultiple orientations by which a tool tip can reach a certain area orstructure within a joint, which can aid the surgeon in positioning toolswith increased accuracy according to a preoperative plan or based on thespecific determined state or step in the surgical procedure.Accordingly, with this technology, simulated projected views of detectedanatomical structures, analytical information, and/or guidanceinformation can be included in overlays that are merged withintraoperative video data based on an automated registration of theanatomical structures, without fixation of fiducials to patient anatomy,to more effectively assist surgical procedures and improve associatedaccuracy and outcomes.

FIG. 12 illustrates a block diagram of an illustrative data processingsystem 1200 in which aspects of the illustrative embodiments areimplemented. The data processing system 1200 is an example of acomputer, such as a server or client, in which computer usable code orinstructions implementing the process for illustrative embodiments ofthe present invention are located. In some embodiments, the dataprocessing system 1200 may be a server computing device. For example,data processing system 1200 can be implemented in a server or anothersimilar computing device operably connected to a CASS 100 as describedabove. The data processing system 1200 can be configured to, forexample, transmit and receive information related to a patient and/or arelated surgical plan with the CASS 100. Accordingly, the dataprocessing system 1200 can be the surgical computer 150, integral withthe surgical computer, or communicably coupled to the surgical computer150 in some examples. In other examples, the data processing system 1200can be integral with or communicably coupled to the optical trackingsystem 115, and other types of deployments can also be used in otherexamples.

In the depicted example, data processing system 1200 can employ a hubarchitecture including a north bridge and memory controller hub (NB/MCH)1201 and south bridge and input/output (I/O) controller hub (SB/ICH)1202. Processing unit 1203 (e.g., one or more central processing unitsor processor cores), main memory 1204, and graphics processor 1205 canbe connected to the NB/MCH 1201. Graphics processor 1205 can beconnected to the NB/MCH 1201 through, for example, an acceleratedgraphics port (AGP).

In the depicted example, a network adapter 1206 connects to the SB/ICH1202. An audio adapter 1207, keyboard and mouse adapter 1208, modem1209, read only memory (ROM) 1210, hard disk drive (HDD) 1211, opticaldrive (e.g., CD or DVD) 1212, universal serial bus (USB) ports and othercommunication ports 1213, and PCI/PCIe devices 1214 may connect to theSB/ICH 1202 through bus system 1216. PCI/PCIe devices 1214 may includeEthernet adapters, add-in cards, and PC cards for notebook computers.ROM 1210 may be, for example, a flash basic input/output system (BIOS).The HDD 1211 and optical drive 1212 can use an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. A super I/O (SIO) device 1215 can be connected to the SB/ICH1202.

An operating system can run on the processing unit 1203. The operatingsystem can coordinate and provide control of various components withinthe data processing system 1200. As a client, the operating system canbe a commercially available operating system. An object-orientedprogramming system, such as the JavaTM programming system, may run inconjunction with the operating system and provide calls to the operatingsystem from the object-oriented programs or applications executing onthe data processing system 1200. As a server, the data processing system1200 can be an IBM® eServerTM System® running the Advanced InteractiveExecutive operating system or the Linux operating system. The dataprocessing system 1200 can be a symmetric multiprocessor (SMP) systemthat can include a plurality of processors in the processing unit 1203.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as the HDD 1211, and are loaded into the main memory 1204 forexecution by the processing unit 1203. The processes for embodimentsdescribed herein (e.g., with reference to FIGS. 8-10 ) can be performedby the processing unit 1203 using computer usable program code, whichcan be located in a memory (e.g., a non-transitory computer readablemedium portion thereof) such as, for example, main memory 1204, ROM1210, or in one or more peripheral devices.

A bus system 1216 can be comprised of one or more busses. The bus system1216 can be implemented using any type of communication fabric orarchitecture that can provide for a transfer of data between differentcomponents or devices attached to the fabric or architecture. Acommunication unit such as the modem 1209 or the network adapter 1206can include one or more devices that can be used to transmit and receivedata.

As described and illustrated in detail above, with this technology,preoperative imaging is used to capture anatomy including bone,cartilage, ligaments, and other anatomical structures, which aresegmented into volumes or surfaces to depict the anatomy in threedimensions. During arthroscopic procedures, as the arthroscope is usedto view a joint, for example, machine learning model(s) are used toautomatically identify the structures depicted in two dimensions in thearthroscopic video data. With the identification of the anatomicalstructures, this technology advantageously facilitates registration ofthe anatomical structures depicted in the arthroscopic video data to thethree dimensional models generated preoperatively. While preoperativemodels are used in many of the examples described and illustratedherein, the identification of anatomical structures depicted in thearthroscopic video data intraoperatively using this technology canfacilitate registration of the anatomical structures depicted in thearthroscopic video data to three dimensional models generatedintraoperatively (e.g., using statistical shape or atlas based models)in other examples. With the orientation of various anatomical structuresrelative to the arthroscope position, as the anatomical structures movein real-time, they can be tracked.

Accordingly, this technology advantageously does not require anyphysical markers placed within or affixed to patient anatomy in order tofacilitate surgical tracking, which reduces the risk of damage andcomplications from surgical procedures. Surgical tracking is alsofacilitated more efficiently because fiducial markers do not need to beplaced within or affixed to anatomical structures and the registrationcan occur automatically during a diagnostic portion of an arthroscopicprocedure.

While various illustrative embodiments incorporating the principles ofthe present teachings have been disclosed, the present teachings are notlimited to the disclosed embodiments. Instead, this application isintended to cover any variations, uses, or adaptations of the presentteachings and use its general principles. Further, this application isintended to cover such departures from the present disclosure that arewithin known or customary practice in the art to which these teachingspertain.

In the above detailed description, reference is made to the accompanyingdrawings, which form a part hereof. In the drawings, similar symbolstypically identify similar components, unless context dictatesotherwise. The illustrative embodiments described in the presentdisclosure are not meant to be limiting. Other embodiments may be used,and other changes may be made, without departing from the spirit orscope of the subject matter presented herein. It will be readilyunderstood that various features of the present disclosure, as generallydescribed herein, and illustrated in the Figures, can be arranged,substituted, combined, separated, and designed in a wide variety ofdifferent configurations, all of which are explicitly contemplatedherein.

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various features. Many modifications and variations canbe made without departing from its spirit and scope, as will be apparentto those skilled in the art. Functionally equivalent methods andapparatuses within the scope of the disclosure, in addition to thoseenumerated herein, will be apparent to those skilled in the art from theforegoing descriptions. It is to be understood that this disclosure isnot limited to particular methods, reagents, compounds, compositions orbiological systems, which can, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein are generally intended as “open” terms (for example, theterm “including” should be interpreted as “including but not limitedto,” the term “having” should be interpreted as “having at least,” theterm “includes” should be interpreted as “includes but is not limitedto,” et cetera). While various compositions, methods, and devices aredescribed in terms of “comprising” various components or steps(interpreted as meaning “including, but not limited to”), thecompositions, methods, and devices can also “consist essentially of” or“consist of” the various components and steps, and such terminologyshould be interpreted as defining essentially closed-member groups.

In addition, even if a specific number is explicitly recited, thoseskilled in the art will recognize that such recitation should beinterpreted to mean at least the recited number (for example, the barerecitation of “two recitations,” without other modifiers, means at leasttwo recitations, or two or more recitations). Furthermore, in thoseinstances where a convention analogous to “at least one of A, B, and C,et cetera” is used, in general such a construction is intended in thesense one having skill in the art would understand the convention (forexample, “a system having at least one of A, B, and C” would include butnot be limited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, et cetera). In those instances where a convention analogous to“at least one of A, B, or C, et cetera” is used, in general such aconstruction is intended in the sense one having skill in the art wouldunderstand the convention (for example, “a system having at least one ofA, B, or C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, et cetera). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, sample embodiments, or drawings, should be understood tocontemplate the possibilities of including one of the terms, either ofthe terms, or both terms. For example, the phrase “A or B” will beunderstood to include the possibilities of “A” or “B” or “A and B.”

In addition, where features of the disclosure are described in terms ofMarkush groups, those skilled in the art will recognize that thedisclosure is also thereby described in terms of any individual memberor subgroup of members of the Markush group.

As will be understood by one skilled in the art, for any and allpurposes, such as in terms of providing a written description, allranges disclosed herein also encompass any and all possible subrangesand combinations of subranges thereof. Any listed range can be easilyrecognized as sufficiently describing and enabling the same range beingbroken down into at least equal halves, thirds, quarters, fifths,tenths, et cetera. As a non-limiting example, each range discussedherein can be readily broken down into a lower third, middle third andupper third, et cetera. As will also be understood by one skilled in theart all language such as “up to,” “at least,” and the like include thenumber recited and refer to ranges that can be subsequently broken downinto subranges as discussed above. Finally, as will be understood by oneskilled in the art, a range includes each individual member. Thus, forexample, a group having 1-3 components refers to groups having 1, 2, or3 components. Similarly, a group having 1-5 components refers to groupshaving 1, 2, 3, 4, or 5 components, and so forth.

The term “about,” as used herein, refers to variations in a numericalquantity that can occur, for example, through measuring or handlingprocedures in the real world; through inadvertent error in theseprocedures; through differences in the manufacture, source, or purity ofcompositions or reagents; and the like. Typically, the term “about” asused herein means greater or lesser than the value or range of valuesstated by ⅒ of the stated values, e.g., ±10%. The term “about” alsorefers to variations that would be recognized by one skilled in the artas being equivalent so long as such variations do not encompass knownvalues practiced by the prior art. Each value or range of valuespreceded by the term “about” is also intended to encompass theembodiment of the stated absolute value or range of values. Whether ornot modified by the term “about,” quantitative values recited in thepresent disclosure include equivalents to the recited values, e.g.,variations in the numerical quantity of such values that can occur, butwould be recognized to be equivalents by a person skilled in the art.

Various of the above-disclosed and other features and functions, oralternatives thereof, may be combined into many other different systemsor applications. Various presently unforeseen or unanticipatedalternatives, modifications, variations or improvements therein may besubsequently made by those skilled in the art, each of which is alsointended to be encompassed by the disclosed embodiments.

1. A method for registration of arthroscopic video images topreoperative models, the method implemented by one or more surgicalcomputing devices and comprising: applying a machine learning model todiagnostic video data captured via an arthroscope to identify ananatomical structure represented in the diagnostic video data;registering one of a plurality of anatomical structures in athree-dimensional (3D) anatomical model to the anatomical structurerepresented in the diagnostic video data, wherein the 3D anatomicalmodel is generated from preoperative image data; tracking the anatomicalstructure intraoperatively based on the registration; generating asimulated projected view of the registered one of the plurality ofanatomical structures from the 3D anatomical model based on a determinedorientation of the arthroscope during capture by the arthroscope ofintraoperative video data; and outputting the simulated projected viewscaled and oriented based on one or more landmark features of theanatomical structure extracted from the intraoperative video data. 2.The method of claim 1, further comprising: generating an overlaycomprising the scaled and oriented simulated projected view; merging thegenerated overlay with the intraoperative video data based on theregistration to generate merged video data; and outputting to a displaydevice the merged video data.
 3. The method of claim 2, furthercomprising determining a stage of a surgical procedure based on anobtained surgical plan for the surgical procedure and identification ofthe anatomical structure in the intraoperative video data, wherein thegenerated overlay further comprises guidance extracted from the obtainedsurgical plan.
 4. The method of claim 3, wherein the guidance comprisesone or more of: textual directions associated with a current task in thesurgical procedure; or a visual indication of another one of theplurality of anatomical structures corresponding to a subsequent task inthe surgical procedure.
 5. The method of claim 2, further comprisingobtaining an annotated version of the 3D anatomical model or thepreoperative image data that identifies an anatomical pointcorresponding to a portion of patient anatomy or at least one of the oneor more landmark features, wherein the generated overlay furthercomprises an indication of the anatomical point.
 6. The method of claim1, further comprising training the machine learning model based onadditional video data comprising a plurality of image frames eachcomprising at least one annotated representation of one or more of theplurality of anatomical structures.
 7. The method of claim 2, whereinthe display device comprises a mixed reality headset and the methodfurther comprises: tracking one or more of a position or an orientationof the mixed reality headset; and generating the overlay using a fieldof view of the arthroscope to determine a local reference frame andbased on a known spatial and scale relationship between the arthroscopefield of view and another reference frame of the mixed reality headsetdetermined based on the tracking.
 8. (canceled)
 9. The method of claim1, further comprising: determining an eye position of a user; andoutputting to a projector the scaled and oriented simulated projectedview for projection by the projector onto patient skin based on thedetermined eye position.
 10. (canceled)
 11. The method of claim 1,wherein the anatomical structure represented in the intraoperative videodata comprises soft tissue and the method further comprises: determininga size and position of a first portion of the soft tissue from theintraoperative video data; and applying another machine learning modelto the 3D anatomical model and the determined size and position togenerate a representation of a second portion of the soft tissue in amorphed state, wherein the simulated projected view comprises therepresentation of the second portion of the soft tissue in the morphedstate.
 12. (canceled)
 13. The method of claim 1, further comprising:generating a weighting value for each of a plurality of portions of the3D anatomical model; and generating the simulated projected view toinclude a subset of the plurality of portions based on the weightingvalues.
 14. (canceled)
 15. A surgical computing device, comprising: anon-transitory computer-readable medium comprising programmedinstructions stored thereon for registration of arthroscopic videoimages to preoperative models; and one or more processors coupled to thenon-transitory computer-readable medium and configured to execute thestored programmed instructions, which causes the one or more processorsto: apply a machine learning model to diagnostic video data captured viaan arthroscope to identify an anatomical structure represented in thediagnostic video data; register one of a plurality of anatomicalstructures in a three-dimensional (3D)anatomical model to the anatomicalstructure represented in the diagnostic video data, wherein the 3Danatomical model is generated from preoperative image data; track theanatomical structure intraoperatively based on the registration:generate a simulated projected view of the registered one of theplurality of anatomical structures from the 3D anatomical model based ona determined orientation of the arthroscope during capture by thearthroscope of intraoperative video data; and output the simulatedprojected view scaled and oriented based on one or more landmarkfeatures of the anatomical structure extracted from the intraoperativevideo data.
 16. The surgical computing device of claim 15, wherein thestored programmed instructions further cause the one or more processorsto: generate an overlay comprising the scaled and oriented simulatedprojected view; merge the generated overlay with the intraoperativevideo data based on the registration to generate merged video data; andoutput to a display device the merged video data.
 17. The surgicalcomputing device of claim 16, wherein the stored programmed instructionsfurther cause the one or more processors to determine a stage of asurgical procedure based on an obtained surgical plan for the surgicalprocedure and identification of the anatomical structure in theintraoperative video data, wherein the generated overlay furthercomprises guidance extracted from the obtained surgical plan.
 18. Thesurgical computing device of claim 17, wherein the guidance comprisesone or more of: textual directions associated with a current task in thesurgical procedure; or a visual indication of another one of theplurality of anatomical structures corresponding to a subsequent task inthe surgical procedure.
 19. The surgical computing device of claim 16,wherein the stored programmed instructions further cause the one or moreprocessors to obtain an annotated version of the 3D anatomical model orthe preoperative image data that identifies an anatomical pointcorresponding to a portion of patient anatomy or at least one of the oneor more landmark features, wherein the generated overlay furthercomprises an indication of the anatomical point.
 20. The surgicalcomputing device of claim 15, wherein the stored programmed instructionsfurther cause the one or more processors to train the machine learningmodel based on additional video data comprising a plurality of imageframes each comprising at least one annotated representation of one ormore of the plurality of anatomical structures.
 21. The surgicalcomputing device of claim 16, wherein the display device comprises amixed reality headset and the stored program instructions further causethe one or more processors to: track one or more of a position or anorientation of the mixed reality headset; and generate the overlay usinga field of view of the arthroscope to determine a local reference frameand based on a known spatial and scale relationship between thearthroscope field of view and another reference frame of the mixedreality headset determined based on the tracking.
 22. The surgicalcomputing device of claim 15, wherein the stored programmed instructionsfurther cause the one or more processors to: determine an eye positionof a user; and output to a projector the scaled and oriented simulatedprojected view for projection by the projector onto patient skin basedon the determined eye position.
 23. The surgical computing device ofclaim 15, wherein the anatomical structure represented in theintraoperative video data comprises soft tissue and the storedprogrammed instructions further cause the one or more processors to:determine a size and position of a first portion of the soft tissue fromthe intraoperative video data; and apply another machine learning modelto the 3D anatomical model and the determined size and position togenerate a representation of a second portion of the soft tissue in amorphed state, wherein the simulated projected view comprises therepresentation of the second portion of the soft tissue in the morphedstate.
 24. The surgical computing device of claim 15, wherein the storedprogrammed instructions further cause the one or more processors to:generate a weighting value for each of a plurality of portions of the 3Danatomical model; and generate the simulated projected view to include asubset of the plurality of portions based on the weighting values.